pwc-archive/papers-with-abstracts huggingface dataset:
Co-Speech Gesture Synthesis using Discrete Gesture Token Learning
Authors: Shuhong Lu, Youngwoo Yoon, Andrew Feng
Date: 2023-03-04 00:00:00

Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users. Such capability will improve the impressions of the robots by human users and will find applications in education, training, and medical services. One challenge in learning the co-speech gesture model is that there may be multiple viable gesture motions for the same speech utterance. The deterministic regression methods can not resolve the conflicting samples and may produce over-smoothed or damped motions. We proposed a two-stage model to address this uncertainty issue in gesture synthesis by modeling the gesture segments as discrete latent codes. Our method utilizes RQ-VAE in the first stage to learn a discrete codebook consisting of gesture tokens from training data. In the second stage, a two-level autoregressive transformer model is used to learn the prior distribution of residual codes conditioned on input speech context. Since the inference is formulated as token sampling, multiple gesture sequences could be generated given the same speech input using top-k sampling. The quantitative results and the user study showed the proposed method outperforms the previous methods and is able to generate realistic and diverse gesture motions.
Underwater Camouflage Object Detection Dataset
Authors: Feng Dong, Jinchao Zhu
Date: 2023-03-01 00:00:00
Tasks: Object, object-detection, Object Detection

We have made a dataset of camouflage object detection mainly for complex seabed scenes, and named it UnderWater RGB&Sonar,or UW-RS for short. The UW-RS dataset contains a total of 1972 image data. The dataset mainly consists of two parts, namely underwater optical data part (UW-R dataset) and underwater sonar data part (UW-S dataset).
FTSO: Effective NAS via First Topology Second Operator
Authors: Likang Wang, Lei Chen
Date: 2023-02-28 00:00:00
Tasks: Neural Architecture Search

Existing one-shot neural architecture search (NAS) methods have to conduct a search over a giant super-net, which leads to the huge computational cost. To reduce such cost, in this paper, we propose a method, called FTSO, to divide the whole architecture search into two sub-steps. Specifically, in the first step, we only search for the topology, and in the second step, we search for the operators. FTSO not only reduces NAS's search time from days to 0.68 seconds, but also significantly improves the found architecture's accuracy. Our extensive experiments on ImageNet show that within 18 seconds, FTSO can achieve a 76.4% testing accuracy, 1.5% higher than the SOTA, PC-DARTS. In addition, FTSO can reach a 97.77% testing accuracy, 0.27% higher than the SOTA, with nearly 100% (99.8%) search time saved, when searching on CIFAR10.
A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples
Authors: Yang Liu, Yue-Jie Hou, Chen-Xin Qin, Xin-Hui Li, Si-Jing Li, Bin Wang, Chi-Chun Zhou
Date: 2023-03-02 00:00:00
Tasks: Data Augmentation, Diagnostic, Generative Adversarial Network, Pulmonary Nodules Classification

Detection of pulmonary nodules by CT is used for screening lung cancer in early stages.omputer aided diagnosis (CAD) based on deep-learning method can identify the suspected areas of pulmonary nodules in CT images, thus improving the accuracy and efficiency of CT diagnosis. The accuracy and robustness of deep learning models. Method:In this paper, we explore (1) the data augmentation method based on the generation model and (2) the model structure improvement method based on the embedding mechanism. Two strategies have been introduced in this study: a new data augmentation method and a embedding mechanism. In the augmentation method, a 3D pixel-level statistics algorithm is proposed to generate pulmonary nodule and by combing the faked pulmonary nodule and healthy lung, we generate new pulmonary nodule samples. The embedding mechanism are designed to better understand the meaning of pixels of the pulmonary nodule samples by introducing hidden variables. Result: The result of the 3DVNET model with the augmentation method for pulmonary nodule detection shows that the proposed data augmentation method outperforms the method based on generative adversarial network (GAN) framework, training accuracy improved by 1.5%, and with embedding mechanism for pulmonary nodules classification shows that the embedding mechanism improves the accuracy and robustness for the classification of pulmonary nodules obviously, the model training accuracy is close to 1 and the model testing F1-score is 0.90.Conclusion:he proposed data augmentation method and embedding mechanism are beneficial to improve the accuracy and robustness of the model, and can be further applied in other common diagnostic imaging tasks.
Trajectory-Prediction with Vision: A Survey
Authors: Apoorv Singh
Date: 2023-03-15 00:00:00
Tasks: Autonomous Vehicles, Prediction, Survey, Trajectory Prediction

To plan a safe and efficient route, an autonomous vehicle should anticipate future trajectories of other agents around it. Trajectory prediction is an extremely challenging task which recently gained a lot of attention in the autonomous vehicle research community. Trajectory-prediction forecasts future state of all the dynamic agents in the scene given their current and past states. A good prediction model can prevent collisions on the road, and hence the ultimate goal for autonomous vehicles: Collision rate: collisions per Million miles. The objective of this paper is to provide an overview of the field trajectory-prediction. We categorize the relevant algorithms into different classes so that researchers can follow through the trends in the trajectory-prediction research field. Moreover we also touch upon the background knowledge required to formulate a trajectory-prediction problem.
Named Entity Recognition Based Automatic Generation of Research Highlights
Authors: Tohida Rehman, Debarshi Kumar Sanyal, Prasenjit Majumder, Samiran Chattopadhyay
Date: 2023-02-25 00:00:00
Proceeding: sdp (COLING) 2022 10
Tasks: named-entity-recognition, Named Entity Recognition

A scientific paper is traditionally prefaced by an abstract that summarizes the paper. Recently, research highlights that focus on the main findings of the paper have emerged as a complementary summary in addition to an abstract. However, highlights are not yet as common as abstracts, and are absent in many papers. In this paper, we aim to automatically generate research highlights using different sections of a research paper as input. We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights. In particular, we have used two deep learning-based models: the first is a pointer-generator network, and the second augments the first model with coverage mechanism. We then augment each of the above models with named entity recognition features. The proposed method can be used to produce highlights for papers with missing highlights. Our experiments show that adding named entity information improves the performance of the deep learning-based summarizers in terms of ROUGE, METEOR and BERTScore measures.
An Analysis of Abstractive Text Summarization Using Pre-trained Models
Authors: Tohida Rehman, Suchandan Das, Debarshi Kumar Sanyal, Samiran Chattopadhyay
Date: 2023-02-25 00:00:00
Tasks: Abstractive Text Summarization, Text Summarization

People nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet. Due to explosion in data, it is helpful for users if they are provided relevant summaries of the search results rather than just links to webpages. Text summarization has become a vital approach to help consumers swiftly grasp vast amounts of information.In this paper, different pre-trained models for text summarization are evaluated on different datasets. Specifically, we have used three different pre-trained models, namely, google/pegasus-cnn-dailymail, T5-base, facebook/bart-large-cnn. We have considered three different datasets, namely, CNN-dailymail, SAMSum and BillSum to get the output from the above three models. The pre-trained models are compared over these different datasets, each of 2000 examples, through ROUGH and BLEU metrics.
Towards the Scalable Evaluation of Cooperativeness in Language Models
Authors: Alan Chan, Maxime Riché, Jesse Clifton
Date: 2023-03-16 00:00:00
Tasks: Language Modeling, Language Modelling

It is likely that AI systems driven by pre-trained language models (PLMs) will increasingly be used to assist humans in high-stakes interactions with other agents, such as negotiation or conflict resolution. Consistent with the goals of Cooperative AI \citep{dafoe_open_2020}, we wish to understand and shape the multi-agent behaviors of PLMs in a pro-social manner. An important first step is the evaluation of model behaviour across diverse cooperation problems. Since desired behaviour in an interaction depends upon precise game-theoretic structure, we focus on generating scenarios with particular structures with both crowdworkers and a language model. Our work proceeds as follows. First, we discuss key methodological issues in the generation of scenarios corresponding to particular game-theoretic structures. Second, we employ both crowdworkers and a language model to generate such scenarios. We find that the quality of generations tends to be mediocre in both cases. We additionally get both crowdworkers and a language model to judge whether given scenarios align with their intended game-theoretic structure, finding mixed results depending on the game. Third, we provide a dataset of scenario based on our data generated. We provide both quantitative and qualitative evaluations of UnifiedQA and GPT-3 on this dataset. We find that instruct-tuned models tend to act in a way that could be perceived as cooperative when scaled up, while other models seemed to have flat scaling trends.
Reevaluating Data Partitioning for Emotion Detection in EmoWOZ
Authors: Moeen Mostafavi, Michael D. Porter
Date: 2023-03-15 00:00:00
Tasks: Emotional Intelligence, Emotion Recognition

This paper focuses on the EmoWoz dataset, an extension of MultiWOZ that provides emotion labels for the dialogues. MultiWOZ was partitioned initially for another purpose, resulting in a distributional shift when considering the new purpose of emotion recognition. The emotion tags in EmoWoz are highly imbalanced and unevenly distributed across the partitions, which causes sub-optimal performance and poor comparison of models. We propose a stratified sampling scheme based on emotion tags to address this issue, improve the dataset's distribution, and reduce dataset shift. We also introduce a special technique to handle conversation (sequential) data with many emotional tags. Using our proposed sampling method, models built upon EmoWoz can perform better, making it a more reliable resource for training conversational agents with emotional intelligence. We recommend that future researchers use this new partitioning to ensure consistent and accurate performance evaluations.
Use of Federated Learning and Blockchain towards Securing Financial Services
Authors: Pushpita Chatterjee, Debashis Das, Danda B Rawat
Date: 2023-02-04 00:00:00
Tasks: Federated Learning

In recent days, the proliferation of several existing and new cyber-attacks pose an axiomatic threat to the stability of financial services. It is hard to predict the nature of attacks that can trigger a serious financial crisis. The unprecedented digital transformation to financial services has been accelerated during the COVID-19 pandemic and it is still ongoing. Attackers are taking advantage of this transformation and pose a new global threat to financial stability and integrity. Many large organizations are switching from centralized finance (CeFi) to decentralized finance (DeFi) because decentralized finance has many advantages. Blockchain can bring big and far-reaching effects on the trustworthiness, safety, accessibility, cost-effectiveness, and openness of the financial sector. The present paper gives an in-depth look at how blockchain and federated learning (FL) are used in financial services. It starts with an overview of recent developments in both use cases. This paper explores and discusses existing financial service vulnerabilities, potential threats, and consequent risks. So, we explain the problems that can be fixed in financial services and how blockchain and FL could help solve them. These problems include data protection, storage optimization, and making more money in financial services. We looked at many blockchain-enabled FL methods and came up with some possible solutions that could be used in financial services to solve several challenges like cost-effectiveness, automation, and security control. Finally, we point out some future directions at the end of this study.
Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation
Authors: Joseanne Viana, Hamed Farkhari, Pedro Sebastiao, Luis Miguel Campos, Katerina Koutlia, Biljana Bojovic, Sandra Lagen, Rui Dinis
Date: 2023-03-03 00:00:00
Tasks: Deep Attention

Despite the robust security features inherent in the 5G framework, attackers will still discover ways to disrupt 5G unmanned aerial vehicle (UAV) operations and decrease UAV control communication performance in Air-to-Ground (A2G) links. Operating under the assumption that the 5G UAV communications infrastructure will never be entirely secure, we propose Deep Attention Recognition (DAtR) as a solution to identify attacks based on a small deep network embedded in authenticated UAVs. Our proposed solution uses two observable parameters: the Signal-to-Interference-plus-Noise Ratio (SINR) and the Reference Signal Received Power (RSSI) to recognize attacks under Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and a probabilistic combination of the two conditions. In the tested scenarios, a number of attackers are located in random positions, while their power is varied in each simulation. Moreover, terrestrial users are included in the network to impose additional complexity on attack detection. To improve the systems overall performance in the attack scenarios, we propose complementing the deep network decision with two mechanisms based on data manipulation and majority voting techniques. We compare several performance parameters in our proposed Deep Network. For example, the impact of Long Short-Term-Memory (LSTM) and Attention layers in terms of their overall accuracy, the window size effect, and test the accuracy when only partial data is available in the training process. Finally, we benchmark our deep network with six widely used classifiers regarding classification accuracy. Our algorithms accuracy exceeds 4% compared with the eXtreme Gradient Boosting (XGB) classifier in LoS condition and around 3% in the short distance NLoS condition. Considering the proposed deep network, all other classifiers present lower accuracy than XGB.
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset
Authors: Allena Venkata Sai Abhishek, Dr. Venkateswara Rao Gurrala, Dr. Laxman Sahoo
Date: 2022-05-01 00:00:00
Proceeding: IJCRT 2022 5
Tasks: Classification, Data Augmentation, Data Visualization, Detecting Image Manipulation, Image Augmentation, image-classification, Image Classification, Image Cropping, Image Denoising, Image Generation, Image Manipulation, Image Manipulation Detection, Image Matting, Image Morphing, image smoothing, Image Stitching, Image-Variation, ROI-based image generation

This residual network has been a broad domain of research in deep learning. Many complex architectures are based upon
residual networks. Residual networks are efficient due to skip connections. This paper highlights the addition of a sequential layer
to the traditional RESNET 18 model for computing the accuracy of an Image classification dataset. The classification datasets such
as Intel Scene dataset, CIFAR10 dataset, etc. These datasets consist of images belonging to various classes. In classification, we
assign the pictures to their respective categories. The addition of a sequential layer gives the accuracy in the range of 0 to 1, which
helps find the accuracy of prediction for the test set.
Detectron2 Object Detection & Manipulating Images using Cartoonization
Authors: Allena Venkata Sai Abhishek, Sonali Kotni
Date: 2021-08-01 00:00:00
Proceeding: International Journal of Engineering Research & Technology (IJERT) 2021 8
Tasks: Autonomous Vehicles, Data Visualization, Image Augmentation, Image Classification, Image Manipulation, Image Segmentation, Instance Segmentation, Object, object-detection, Object Detection, Object Recognition, Panoptic Segmentation, Real-Time Object Detection, Semantic Segmentation

In today's world, there is a rapid increase in the autonomous vehicle. There are various levels of autonomous vehicles depending upon the degree of autonomy-for the lower degree of autonomy driver has more power and functionality for managing, on coming to the fully automated vehicle like Tesla are expected to have full control over the functions. These advances cooperate to plan the vehicle's position and its nearness to everything around it. Because of this, there is popularity for these vehicles, since they give a great deal of advantages to individuals utilizing them. We use the Facebook AI Research software system that implements object detection algorithms, Caffe2 deep learning framework for advanced object detection by offering speedy training. We have also manipulated images to derive insights addressing the issues companies face when making the step from research to production. We have implemented detectron2 object detection for faster detection of objects. There is labeling of the object & we used manipulation of images using cartoonization.
MLA-Net: Feature Pyramid Network with Multi-Level Local Attention for Object Detection
Authors: Xiaobao Yang 1, 2, *, Wentao Wang 3, Junsheng Wu 4, Chen Ding 3, Sugang Ma 3 and Zhiqiang Hou 3
Date: 2022-12-06 00:00:00
Proceeding: Mathematics 2022 12
Tasks: object-detection, Object Detection

Abstract: Feature pyramid networks and attention mechanisms are the mainstream methods to
improve the detection performance of many current models. However, when they are learned
jointly, there is a lack of information association between multi-level features. Therefore, this paper
proposes a feature pyramid of the multi-level local attention method, dubbed as MLA-Net (Feature
Pyramid Network with Multi-Level Local Attention for Object Detection), which aims to establish a
correlation mechanism for multi-level local information. First, the original multi-level features are
deformed and rectified using the local pixel-rectification module, and global semantic enhancement is
achieved through the multi-level spatial-attention module. After that, the original features are further
fused through the residual connection to achieve the fusion of contextual features to enhance the
feature representation. Extensive ablation experiments were conducted on the MS COCO (Microsoft
Common Objects in Context) dataset, and the results demonstrate the effectiveness of the proposed
method with a 0.5% enhancement. An improvement of 1.2% was obtained on the PASCAL VOC
(Pattern Analysis Statistical Modelling and Computational Learning, Visual Object Classes) dataset,
reaching 81.8%, thereby, indicating that the proposed method is robust and can compete with other
advanced detection models.
Kernel-predicting convolutional networks for denoising monte carlo renderings.
Authors: Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony Derose, Fabrice Rousselle
Date: 2017-07-20 00:00:00
Proceeding: ACM Transactions on Graphics 2017 7
Tasks: Denoising

Regression-based algorithms have shown to be good at denoising Monte Carlo (MC) renderings by leveraging its inexpensive by-products (e.g., feature buffers). However, when using higher-order models to handle complex cases, these techniques often overfit to noise in the input. For this reason, supervised learning methods have been proposed that train on a large collection of reference examples, but they use explicit filters that limit their denoising ability. To address these problems, we propose a novel, supervised learning approach that allows the filtering kernel to be more complex and general by leveraging a deep convolutional neural network (CNN) architecture. In one embodiment of our framework, the CNN directly predicts the final denoised pixel value as a highly non-linear combination of the input features. In a second approach, we introduce a novel, kernel-prediction network which uses the CNN to estimate the local weighting kernels used to compute each denoised pixel from its neighbors. We train and evaluate our networks on production data and observe improvements over state-of-the-art MC denoisers, showing that our methods generalize well to a variety of scenes. We conclude by analyzing various components of our architecture and identify areas of further research in deep learning for MC denoising.
Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering
Authors: Zhe Xue, Junping Du, Hai Zhu, Zhongchao Guan, Yunfei Long, Yu Zang, Meiyu Liang
Date: 2022-10-10 00:00:00
Proceeding: ACM International Conference on Multimedia 2022 10
Tasks: Clustering, Contrastive Learning, Incomplete multi-view clustering, Representation Learning

Incomplete multi-view clustering is a challenging task which aims to partition the unlabeled incomplete multi-view data into several clusters. The existing incomplete multi-view clustering methods neglect to utilize the diversified correlations inherent in data and handle the noise contained in different views. To address these issues, we propose a Robust Diversified Graph Contrastive Network (RDGC) for incomplete multi-view clustering, which integrates multi-view representation learning and diversified graph contrastive regularization into a unified framework. Multi-view unified and specific encoding network is developed to fuse different views into a unified representation, which can flexibly estimate the importance of views for incomplete multi-view data. Robust diversified graph contrastive regularization is proposed which captures the diversified data correlations to improve the discriminating power of the learned representation and reduce the information loss caused by the view missing problem. Moreover, our method can effectively resist the influence of noise and unreliable views by leveraging the robust contrastive learning loss. Extensive experiments conducted on four multi-view clustering datasets demonstrate the superiority of our method over the state-of-the-art methods.