pwc-archive/papers-with-abstracts huggingface dataset:
Inconsistencies in the Definition and Annotation of Student Engagement in Virtual Learning Datasets: A Critical Review
Authors: Shehroz S. Khan, Ali Abedi, Tracey Colella
Date: 2022-08-09 00:00:00

Background: Student engagement (SE) in virtual learning can have a major impact on meeting learning objectives and program dropout risks. Developing Artificial Intelligence (AI) models for automatic SE measurement requires annotated datasets. However, existing SE datasets suffer from inconsistent definitions and annotation protocols mostly unaligned with the definition of SE in educational psychology. This issue could be misleading in developing generalizable AI models and make it hard to compare the performance of these models developed on different datasets. The objective of this critical review was to explore the existing SE datasets and highlight inconsistencies in terms of differing engagement definitions and annotation protocols. Methods: Several academic databases were searched for publications introducing new SE datasets. The datasets containing students' single- or multi-modal data in online or offline computer-based virtual learning sessions were included. The definition and annotation of SE in the existing datasets were analyzed based on our defined seven dimensions of engagement annotation: sources, data modalities, timing, temporal resolution, level of abstraction, combination, and quantification. Results: Thirty SE measurement datasets met the inclusion criteria. The reviewed SE datasets used very diverse and inconsistent definitions and annotation protocols. Unexpectedly, very few of the reviewed datasets used existing psychometrically validated scales in their definition of SE. Discussion: The inconsistent definition and annotation of SE are problematic for research on developing comparable AI models for automatic SE measurement. Some of the existing SE definitions and protocols in settings other than virtual learning that have the potential to be used in virtual learning are introduced.
Using Large Context for Kidney Multi-Structure Segmentation from CTA Images
Authors: Weiwei Cao, Yuzhu Cao
Date: 2022-08-09 00:00:00
Tasks: Position

Accurate and automated segmentation of multi-structure (i.e., kidneys, renal tu-mors, arteries, and veins) from 3D CTA is one of the most important tasks for surgery-based renal cancer treatment (e.g., laparoscopic partial nephrectomy). This paper briefly presents the main technique details of the multi-structure seg-mentation method in MICCAI 2022 KIPA challenge. The main contribution of this paper is that we design the 3D UNet with the large context information cap-turing capability. Our method ranked eighth on the MICCAI 2022 KIPA chal-lenge open testing dataset with a mean position of 8.2. Our code and trained models are publicly available at https://github.com/fengjiejiejiejie/kipa22_nnunet.
Aesthetic Attributes Assessment of Images with AMANv2 and DPC-CaptionsV2
Authors: Xinghui Zhou, Xin Jin, Jianwen Lv, Heng Huang, Ming Mao, Shuai Cui
Date: 2022-08-09 00:00:00
Tasks: Attribute, Image Captioning

Image aesthetic quality assessment is popular during the last decade. Besides numerical assessment, nature language assessment (aesthetic captioning) has been proposed to describe the generally aesthetic impression of an image. In this paper, we propose aesthetic attribute assessment, which is the aesthetic attributes captioning, i.e., to assess the aesthetic attributes such as composition, lighting usage and color arrangement. It is a non-trivial task to label the comments of aesthetic attributes, which limit the scale of the corresponding datasets. We construct a novel dataset, named DPC-CaptionsV2, by a semi-automatic way. The knowledge is transferred from a small-scale dataset with full annotations to large-scale professional comments from a photography website. Images of DPC-CaptionsV2 contain comments up to 4 aesthetic attributes: composition, lighting, color, and subject. Then, we propose a new version of Aesthetic Multi-Attributes Networks (AMANv2) based on the BUTD model and the VLPSA model. AMANv2 fuses features of a mixture of small-scale PCCD dataset with full annotations and large-scale DPCCaptionsV2 dataset with full annotations. The experimental results of DPCCaptionsV2 show that our method can predict the comments on 4 aesthetic attributes, which are closer to aesthetic topics than those produced by the previous AMAN model. Through the evaluation criteria of image captioning, the specially designed AMANv2 model is better to the CNN-LSTM model and the AMAN model.
Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning
Authors: Xin Jin, Shu Zhao, Le Zhang, Xin Zhao, Qiang Deng, Chaoen Xiao
Date: 2022-08-09 00:00:00
Tasks: Attribute, Face Generation, Facial Beauty Prediction, Image Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, image generation has made great strides in improving the quality of images, producing high-fidelity ones. Also, quite recently, there are architecture designs, which enable GAN to unsupervisedly learn the semantic attributes represented in different layers. However, there is still a lack of research on generating face images more consistent with human aesthetics. Based on EigenGAN [He et al., ICCV 2021], we build the techniques of reinforcement learning into the generator of EigenGAN. The agent tries to figure out how to alter the semantic attributes of the generated human faces towards more preferable ones. To accomplish this, we trained an aesthetics scoring model that can conduct facial beauty prediction. We also can utilize this scoring model to analyze the correlation between face attributes and aesthetics scores. Empirically, using off-the-shelf techniques from reinforcement learning would not work well. So instead, we present a new variant incorporating the ingredients emerging in the reinforcement learning communities in recent years. Compared to the original generated images, the adjusted ones show clear distinctions concerning various attributes. Experimental results using the MindSpore, show the effectiveness of the proposed method. Altered facial images are commonly more attractive, with significantly improved aesthetic levels.
Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching
Authors: Yikai Bian, Le Hui, Jianjun Qian, Jin Xie
Date: 2022-08-09 00:00:00
Tasks: Domain Adaptation, Graph Matching, Semantic Segmentation, Unsupervised Domain Adaptation

Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data. Most of existing methods use global-level feature alignment to transfer the knowledge from the source domain to the target domain, which may cause the semantic ambiguity of the feature space. In this paper, we propose a graph-based framework to explore the local-level feature alignment between the two domains, which can reserve semantic discrimination during adaptation. Specifically, in order to extract local-level features, we first dynamically construct local feature graphs on both domains and build a memory bank with the graphs from the source domain. In particular, we use optimal transport to generate the graph matching pairs. Then, based on the assignment matrix, we can align the feature distributions between the two domains with the graph-based local feature loss. Furthermore, we consider the correlation between the features of different categories and formulate a category-guided contrastive loss to guide the segmentation model to learn discriminative features on the target domain. Extensive experiments on different synthetic-to-real and real-to-real domain adaptation scenarios demonstrate that our method can achieve state-of-the-art performance.
Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network
Authors: Desen Meng, Feng Gao, Junyu Dong, Qian Du, Heng-Chao Li
Date: 2022-08-09 00:00:00
Tasks: Change Detection

Recently, change detection methods for synthetic aperture radar (SAR) images based on convolutional neural networks (CNN) have gained increasing research attention. However, existing CNN-based methods neglect the interactions among multilayer convolutions, and errors involved in the preclassification restrict the network optimization. To this end, we proposed a layer attention-based noise-tolerant network, termed LANTNet. In particular, we design a layer attention module that adaptively weights the feature of different convolution layers. In addition, we design a noise-tolerant loss function that effectively suppresses the impact of noisy labels. Therefore, the model is insensitive to noisy labels in the preclassification results. The experimental results on three SAR datasets show that the proposed LANTNet performs better compared to several state-of-the-art methods. The source codes are available at https://github.com/summitgao/LANTNet
In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation
Authors: Bolin Lai, Miao Liu, Fiona Ryan, James M. Rehg
Date: 2022-08-08 00:00:00
Tasks: Gaze Estimation

In this paper, we present the first transformer-based model to address the challenging problem of egocentric gaze estimation. We observe that the connection between the global scene context and local visual information is vital for localizing the gaze fixation from egocentric video frames. To this end, we design the transformer encoder to embed the global context as one additional visual token and further propose a novel Global-Local Correlation (GLC) module to explicitly model the correlation of the global token and each local token. We validate our model on two egocentric video datasets - EGTEA Gaze+ and Ego4D. Our detailed ablation studies demonstrate the benefits of our method. In addition, our approach exceeds previous state-of-the-arts by a large margin. We also provide additional visualizations to support our claim that global-local correlation serves a key representation for predicting gaze fixation from egocentric videos. More details can be found in our website (https://bolinlai.github.io/GLC-EgoGazeEst).
NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks
Authors: Doyub Kim, Minjae Lee, Ken Museth
Date: 2022-08-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB [Museth 2013], by leveraging recent advancements in machine learning. Our novel hybrid data structure can reduce the memory footprints of VDB volumes by orders of magnitude, while maintaining its flexibility and only incurring small (user-controlled) compression errors. Specifically, NeuralVDB replaces the lower nodes of a shallow and wide VDB tree structure with multiple hierarchical neural networks that separately encode topology and value information by means of neural classifiers and regressors respectively. This approach is proven to maximize the compression ratio while maintaining the spatial adaptivity offered by the higher-level VDB data structure. For sparse signed distance fields and density volumes, we have observed compression ratios on the order of 10x to more than 100x from already compressed VDB inputs, with little to no visual artifacts. Furthermore, NeuralVDB is shown to offer more effective compression performance compared to other neural representations such as Neural Geometric Level of Detail [Takikawa et al. 2021], Variable Bitrate Neural Fields [Takikawa et al. 2022a], and Instant Neural Graphics Primitives [M\"uller et al. 2022]. Finally, we demonstrate how warm-starting from previous frames can accelerate training, i.e., compression, of animated volumes as well as improve temporal coherency of model inference, i.e., decompression.
Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks
Authors: Yonghao Xu, Weikang Yu, Pedram Ghamisi, Michael Kopp, Sepp Hochreiter
Date: 2022-08-08 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation, zero-shot-classification, Zero-Shot Learning

The synthesis of high-resolution remote sensing images based on text descriptions has great potential in many practical application scenarios. Although deep neural networks have achieved great success in many important remote sensing tasks, generating realistic remote sensing images from text descriptions is still very difficult. To address this challenge, we propose a novel text-to-image modern Hopfield network (Txt2Img-MHN). The main idea of Txt2Img-MHN is to conduct hierarchical prototype learning on both text and image embeddings with modern Hopfield layers. Instead of directly learning concrete but highly diverse text-image joint feature representations for different semantics, Txt2Img-MHN aims to learn the most representative prototypes from text-image embeddings, achieving a coarse-to-fine learning strategy. These learned prototypes can then be utilized to represent more complex semantics in the text-to-image generation task. To better evaluate the realism and semantic consistency of the generated images, we further conduct zero-shot classification on real remote sensing data using the classification model trained on synthesized images. Despite its simplicity, we find that the overall accuracy in the zero-shot classification may serve as a good metric to evaluate the ability to generate an image from text. Extensive experiments on the benchmark remote sensing text-image dataset demonstrate that the proposed Txt2Img-MHN can generate more realistic remote sensing images than existing methods. Code and pre-trained models are available online (https://github.com/YonghaoXu/Txt2Img-MHN).
Occlusion-Aware Instance Segmentation via BiLayer Network Architectures
Authors: Lei Ke, Yu-Wing Tai, Chi-Keung Tang
Date: 2022-08-08 00:00:00
Tasks: Human Instance Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation, Video Instance Segmentation

Segmenting highly-overlapping image objects is challenging, because there is typically no distinction between real object contours and occlusion boundaries on images. Unlike previous instance segmentation methods, we model image formation as a composition of two overlapping layers, and propose Bilayer Convolutional Network (BCNet), where the top layer detects occluding objects (occluders) and the bottom layer infers partially occluded instances (occludees). The explicit modeling of occlusion relationship with bilayer structure naturally decouples the boundaries of both the occluding and occluded instances, and considers the interaction between them during mask regression. We investigate the efficacy of bilayer structure using two popular convolutional network designs, namely, Fully Convolutional Network (FCN) and Graph Convolutional Network (GCN). Further, we formulate bilayer decoupling using the vision transformer (ViT), by representing instances in the image as separate learnable occluder and occludee queries. Large and consistent improvements using one/two-stage and query-based object detectors with various backbones and network layer choices validate the generalization ability of bilayer decoupling, as shown by extensive experiments on image instance segmentation benchmarks (COCO, KINS, COCOA) and video instance segmentation benchmarks (YTVIS, OVIS, BDD100K MOTS), especially for heavy occlusion cases. Code and data are available at https://github.com/lkeab/BCNet.
Contrast-Phys: Unsupervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast
Authors: Zhaodong Sun, Xiaobai Li
Date: 2022-08-08 00:00:00

Video-based remote physiological measurement utilizes face videos to measure the blood volume change signal, which is also called remote photoplethysmography (rPPG). Supervised methods for rPPG measurements achieve state-of-the-art performance. However, supervised rPPG methods require face videos and ground truth physiological signals for model training. In this paper, we propose an unsupervised rPPG measurement method that does not require ground truth signals for training. We use a 3DCNN model to generate multiple rPPG signals from each video in different spatiotemporal locations and train the model with a contrastive loss where rPPG signals from the same video are pulled together while those from different videos are pushed away. We test on five public datasets, including RGB videos and NIR videos. The results show that our method outperforms the previous unsupervised baseline and achieves accuracies very close to the current best supervised rPPG methods on all five datasets. Furthermore, we also demonstrate that our approach can run at a much faster speed and is more robust to noises than the previous unsupervised baseline. Our code is available at https://github.com/zhaodongsun/contrast-phys.
Understanding Weight Similarity of Neural Networks via Chain Normalization Rule and Hypothesis-Training-Testing
Authors: Guangcong Wang, Guangrun Wang, Wenqi Liang, JianHuang Lai
Date: 2022-08-08 00:00:00
Tasks: Representation Learning

We present a weight similarity measure method that can quantify the weight similarity of non-convex neural networks. To understand the weight similarity of different trained models, we propose to extract the feature representation from the weights of neural networks. We first normalize the weights of neural networks by introducing a chain normalization rule, which is used for weight representation learning and weight similarity measure. We extend the traditional hypothesis-testing method to a hypothesis-training-testing statistical inference method to validate the hypothesis on the weight similarity of neural networks. With the chain normalization rule and the new statistical inference, we study the weight similarity measure on Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), and find that the weights of an identical neural network optimized with the Stochastic Gradient Descent (SGD) algorithm converge to a similar local solution in a metric space. The weight similarity measure provides more insight into the local solutions of neural networks. Experiments on several datasets consistently validate the hypothesis of weight similarity measure.
Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors
Authors: Andrea Panizza, Szymon Tomasz Stefanek, Stefano Melacci, Giacomo Veneri, Marco Gori
Date: 2022-08-08 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

Nondestructive testing (NDT) is widely applied to defect identification of turbine components during manufacturing and operation. Operational efficiency is key for gas turbine OEM (Original Equipment Manufacturers). Automating the inspection process as much as possible, while minimizing the uncertainties involved, is thus crucial. We propose a model based on RetinaNet to identify drilling defects in X-ray images of turbine blades. The application is challenging due to the large image resolutions in which defects are very small and hardly captured by the commonly used anchor sizes, and also due to the small size of the available dataset. As a matter of fact, all these issues are pretty common in the application of Deep Learning-based object detection models to industrial defect data. We overcome such issues using open source models, splitting the input images into tiles and scaling them up, applying heavy data augmentation, and optimizing the anchor size and aspect ratios with a differential evolution solver. We validate the model with $3$-fold cross-validation, showing a very high accuracy in identifying images with defects. We also define a set of best practices which can help other practitioners overcome similar challenges.
Semi-Supervised Cross-Modal Salient Object Detection with U-Structure Networks
Authors: Yunqing Bao, Hang Dai, Abdulmotaleb Elsaddik
Date: 2022-08-08 00:00:00
Tasks: object-detection, Object Detection, Salient Object Detection

Salient Object Detection (SOD) is a popular and important topic aimed at precise detection and segmentation of the interesting regions in the images. We integrate the linguistic information into the vision-based U-Structure networks designed for salient object detection tasks. The experiments are based on the newly created DUTS Cross Modal (DUTS-CM) dataset, which contains both visual and linguistic labels. We propose a new module called efficient Cross-Modal Self-Attention (eCMSA) to combine visual and linguistic features and improve the performance of the original U-structure networks. Meanwhile, to reduce the heavy burden of labeling, we employ a semi-supervised learning method by training an image caption model based on the DUTS-CM dataset, which can automatically label other datasets like DUT-OMRON and HKU-IS. The comprehensive experiments show that the performance of SOD can be improved with the natural language input and is competitive compared with other SOD methods.
Rethinking Robust Representation Learning Under Fine-grained Noisy Faces
Authors: Bingqi Ma, Guanglu Song, Boxiao Liu, Yu Liu
Date: 2022-08-08 00:00:00
Tasks: Face Recognition, Representation Learning

Learning robust feature representation from large-scale noisy faces stands out as one of the key challenges in high-performance face recognition. Recent attempts have been made to cope with this challenge by alleviating the intra-class conflict and inter-class conflict. However, the unconstrained noise type in each conflict still makes it difficult for these algorithms to perform well. To better understand this, we reformulate the noise type of each class in a more fine-grained manner as N-identities|K^C-clusters. Different types of noisy faces can be generated by adjusting the values of \nkc. Based on this unified formulation, we found that the main barrier behind the noise-robust representation learning is the flexibility of the algorithm under different N, K, and C. For this potential problem, we propose a new method, named Evolving Sub-centers Learning~(ESL), to find optimal hyperplanes to accurately describe the latent space of massive noisy faces. More specifically, we initialize M sub-centers for each class and ESL encourages it to be automatically aligned to N-identities|K^C-clusters faces via producing, merging, and dropping operations. Images belonging to the same identity in noisy faces can effectively converge to the same sub-center and samples with different identities will be pushed away. We inspect its effectiveness with an elaborate ablation study on the synthetic noisy dataset with different N, K, and C. Without any bells and whistles, ESL can achieve significant performance gains over state-of-the-art methods on large-scale noisy faces