Reviewerly/exHarmony huggingface dataset:
Grassmann Graph Embedding for Few-Shot Class Incremental Learning

Few-shot class incremental learning (FSCIL) poses a significant challenge in machine learning as it involves acquiring new knosstabwledge from limited samples while retaining previous knowledge. However, the scarcity of data for new classes not only leads to overfitting but also exacerbates catastrophic forgetting during incremental learning. To tackle these challenges, we propose the Grassmann Graph Embedding framework for Few-shot Class Incremental Learning (GGE-FSCIL), which effectively preserves the geometric properties and structural relationships of learned knowledge. Unlike most existing approaches that optimize network parameters in the Euclidean space, we leverage manifold space for optimizing the incremental learning network. Specifically, we embed the approximated characteristics of each class onto a Grassmann manifold, enabling the preservation of intra-class knowledge and enhancing adaptability to new tasks with few-shot samples. Recognizing that knowledge exists within interconnected relationships, we construct a neighborhood graph on the Grassmann manifold to maintain inter-class structure information, thereby alleviating catastrophic forgetting. We extensively evaluated our method on CIFAR100, miniImageNet, and CUB200 datasets, and the results demonstrate that our approach surpasses the state-of-the-art methods in few-shot class incremental learning.
Seeing is No Longer Believing: A Survey on the State of Deepfakes, AI-Generated Humans, and Other Nonveridical Media

Did you see that crazy photo of Chris Hemsworth wearing a gorgeous, blue ballgown? What about the leaked photo of Bernie Sanders dancing with Sarah Palin? If these don’t sound familiar, it’s because these events never happened–but with text-to-image generators and deepfake AI technologies, it is effortless for anyone to produce such images. Over the last decade, there has been an explosive rise in research papers, as well as tool development and usage, dedicated to deepfakes, text-to-image generation, and image synthesis. These tools provide users with great creative power, but with that power comes “great responsibility;” it is just as easy to produce nefarious and misleading content as it is to produce comedic or artistic content. Therefore, given the recent advances in the field, it is important to assess the impact they may have. In this paper, we conduct meta-research on deepfakes to visualize the evolution of these tools and paper publications. We also identify key authors, research institutions, and papers based on bibliometric data. Finally, we conduct a survey that tests the ability of participants to distinguish photos of real people from fake, AI-generated images of people. Based on our meta-research, survey, and background study, we conclude that humans are falling behind in the race to keep up with AI, and we must be conscious of the societal impact.
Teacher Digital Competence Analysis in Block Programming Applied to Educational Robotics

This research aims to analyze digital teaching competence in the field of block programming applied to educational robotics among active primary education teachers. The research methodology employed is defined as descriptive and exploratory. We gathered quantitative data through a structured questionnaire, reflecting the level of digital competence of the participating teachers. The results obtained from this study reveal significant deficiencies in the development of teaching digital competence, especially in the areas of robotics and educational programming. Importantly, the teachers in the sample exhibit a high level of knowledge in active pedagogies. In conclusion, we highlight the need to design training and professional development programs that enhance the digital competence of active primary education teachers, with an emphasis on training schemes geared towards robotics and educational programming.
Interventional Supervised Learning for Person Re-identification

Person re-identification (ReID) is a problem of retrieving pedestrian images. The complexity of ReID is attributed to image resolution, shooting angles, and object occlusion. With the evolution of computer vision, researchers have proposed effective solutions to tackle ReID. Causal inference has demonstrated its utility across various computer vision tasks. This paper investigates the potential of causal inference technology to enhance ReID performance. We construct a structural causal model for ReID and determine that dataset knowledge can constrain models from grasping genuine causality by affecting image features. Consequently, we propose intervention-based methods for supervised learning ReID (ISReID), introducing three effective backdoor adjustment schemes to intervene in supervised learning ReID models. To evaluate our approach’s effectiveness, we conduct experiments on four widely-used ReID datasets: DukeMTMC-reID, Market-1501, CUHK03, and MSMT17. The experimental results confirm the efficacy of our backdoor adjustment schemes in enhancing supervised learning ReID models’ performance. The source code can be found at https://github.com/SCNU203/ISReID .
Global Variational Convolution Network for Semi-supervised Node Classification on Large-Scale Graphs

Graph Neural Networks (GNNs) have received much attention in the graph deep learning. However, there are some issues in extending traditional aggregation-based GNNs to large-scale graphs. With the rapid increase of neighborhood width, we find that the direction of aggregation can be disrupted and quite unbalanced, which compromises graphic structure and feature representation. This phenomenon is referred to Receptive Field Collapse. In order to preserve more structural information on large-scale graphs, we propose a novel Global Variational Convolutional Networks (GVCNs) for Semi-Supervised Node Classifications, which consists of a variational aggregation mechanism and a guidance learning mechanism. Variational aggregation can moderately map the unbalanced neighborhood distribution to a prior distribution. And the guidance learning mechanism, based on positive pointwise mutual information (PPMI), encourages the model to concentrate on more prominent graphic structures, which increases information entropy and alleviates Receptive Field Collapse. In addition, we propose a variational convolutional kernel to achieve effective global aggregation. Finally, we evaluate GVCNs on the Open Graph Benchmark (OGB) Arxiv and Products datasets. Up to the submission date (Jan 20, 2023), GVCNs achieve significant performance improvements compared to other aggregation-based GNNs, even state-of-the-art decoupling-based methods, the performance of GVCNs remains competitive with moderate spatiotemporal complexity. Our code can be obtained from: https://github.com/Yide-Qiu/GVCN .
SemiRefiner: Learning to Refine Semi-realistic Paintings

The previous image optimization methods cannot complete the automatic refinement of semi-realistic paintings. Aiming at improving the efficiency of refinement manually, we propose an automatic refinement method for semi-realistic figure paintings guided by the line art. In order to enable the framework to adjust the draft color in the refinement process like a real painter, we design a color correction module, which automatically fixes the inappropriate color in the draft. In order to reduce artifacts and generate high-quality results, we use the line art to guide the refinement. We further devise a line art optimization module in the framework to ensure generation of high quality results by improving the quality of the line art. The experimental results and user surveys demonstrate the effectiveness of the proposed method.
Frequency Domain Distillation for Data-Free Quantization of Vision Transformer

The increasing size of deep learning models has made model compression techniques increasingly important. Neural network quantization is a technique that can significantly compress models while preserving their original precision. However, conventional quantization methods relies on real training data, making it unsuitable for scenarios where data is unavailable. Data-Free quantization methods address this issue by synthesizing pseudo data to calibrate or fine tune the quantized model. However, these methods overlook an important problem, i.e., the mismatch between the low-frequency and high-frequency components of the synthesized pseudo data. This is due to the simultaneous optimization of low-frequency and high-frequency information, which can interfere with each other. We analyze the reasons behind this phenomenon and propose a frequency domain distillation (FDD) method to address this issue. Specifically, we first optimize the low-frequency component, followed by the high-frequency component, and employ distillation to make the high-frequency component more consistent with the low-frequency component. Additionally, we apply a progressive optimization strategy by gradually increasing the optimized region of pseudo data. We achieved state-of-the-art results on all the Vit models involved in our experiments, and complete ablation study also demonstrated the effectiveness of our method. Our code can be found at here .
A Closer Look at Few-Shot Object Detection

Few-shot object detection, which aims to detect unseen classes in data-scarce scenarios, remains a challenging task. Most existing works adopt Faster RCNN as the basic framework and employ fine-tuning paradigm to tackle this problem. However, the intrinsic concept drift in the Region Proposal Network and the rejection of false positive region proposals hinder model performance. In this paper, we introduce a simple and effective task adapter in RPN, which decouples it from the backbone network to obtain category-agnostic knowledge. In the last two layers of the task adapter, we use large-kernel spatially separable convolution to adaptively detect objects at different scales. In addition, We design an offline structural reparameterization approach to better initialize box classifiers by constructing an augmented dataset to learn initial novel prototypes and explicitly incorporating priors from base training in extremely low-shot scenarios. Extensive experiments on various benchmarks have demonstrated that our proposed method is significantly superior to other methods and is comparative with state-of-the-art performance.
Diffusion-Based Semantic Image Synthesis from Sparse Layouts

We present an efficient framework that utilizes diffusion models to generate landscape images from sparse semantic layouts. Previous approaches use dense semantic label maps to generate images, where the quality of the results is highly dependent on the accuracy of the input semantic layouts. However, it is not trivial to create detailed and accurate semantic layouts in practice. To address this challenge, we carefully design a random masking process that effectively simulates real user input during the model training phase, making it more practical for real-world applications. Our framework leverages the Semantic Diffusion Model (SDM) as a generator to create full landscape images from sparse label maps, which are created randomly during the random masking process. Missing semantic information is complemented based on the learned image structure. Furthermore, we achieve comparable inference speed to GAN-based models through a model distillation process while preserving the generation quality. After training with the well-designed random masking process, our proposed framework is able to generate high-quality landscape images with sparse and intuitive inputs, which is useful for practical applications. Experiments show that our proposed method outperforms existing approaches both quantitatively and qualitatively. Code is available at https://github.com/sky24h/SIS_from_Sparse_Layouts .
Dynamic Gesture Recognition Based on 3D Central Difference Separable Residual LSTM Coordinate Attention Networks

The recognition of dynamic gestures has garnered significant attention in the field of human-computer interaction. However, several factors unrelated to the gestures, such as background, and spatial scale, pose significant challenges in improving the accuracy of recognition, despite the flexibility of the gestures themselves. In this paper, we propose an end-to-end recognition network, named 3D central difference separable residual long and short-term memory (LSTM) coordinate attention (3D CRLCA), which addresses these issues. Our network utilizes 3D central difference separable convolution (3D CDSC) to extract fine-grained spatiotemporal feature information, facilitating the recognition and classification of dynamic gestures. We have also incorporated a residual module in the network to enhance the discriminative ability between gesture categories. To further extract semantic and action information of the gestures, we have combined the LSTM-CA attention mechanism, which enables the network to focus on the gesture area and the temporal and spatial characteristics of gestures, to facilitate gesture recognition. Our experiments on the ChaLearn Large-scale Gesture Recognition Dataset (IsoGD) and IPN dataset demonstrate that our approach outperforms other methods.
Data-Free Low-Bit Quantization via Dynamic Multi-teacher Knowledge Distillation

Data-free quantization is an effective way to compress deep neural networks under the situation where training data is unavailable, due to data privacy and security issues. Although Off-the-shelf data-free quantization methods achieve the relatively same accuracy as the fully-precision (FP) models for high-bit (e.g., 8-bit) quantization, low-bit quantization performance drops significantly to restrict their extensive applications. In this paper, we propose a novel data-free low-bit quantization method via Dynamic Multi-teacher Knowledge Distillation (DMKD) to improve the performance of low-bit quantization models. In particular, we first introduce a generator to synthesize the training data based on the input of random noise. The low-bit quantization models are then trained on these synthetic images by the dynamic knowledge from the FP model and the high-bit quantization models, which are balanced by learnable loss weight factors. The factors are controlled by a tiny learnable FP network to adaptively allocate the balanced weights for the knowledge from the FP model and the high-bit quantization models during training. For inference, we only kept the low-bit quantization model by safely removing other additional networks, such as the generator and the tiny model. Extensive experiments demonstrate the effectiveness of DMKD for low-bit quantization of widely-used convolutional neural networks (CNNs) on different benchmark datasets. Our DMKD ooon methods.
Periodic-Aware Network for Fine-Grained Action Recognition

Recently, skeleton-based action recognition has gained increasing attention and achieved remarkable results in coarse-grained action recognition. Despite the positive results shown in these attempts, they are less effective in scenarios that require a detailed comparison between fine-grained classes, e.g. different moves during a vault. In such scenarios, existing methods make it hard to distinguish subtle differences between actions with different numbers of repetitions. In this article, to solve the above problem, we introduce periodicity into fine-grained action classification and propose a novel network architecture named periodic-aware network (PAN) to distinguish fine-grained actions with different numbers of repetitions. Firstly, a periodicity feature extraction module (PFEM) is proposed to capture periodicity information and extract periodicity features of different levels. Then, a periodicity fusion module (PFM) is proposed to fuse periodicity features and spatiotemporal features. We apply multiple periodicity fusion modules to fuse different levels of features. Finally, the results are obtained by classifying the fusion features. Extensive experiments on two fine-grained skeleton-based action recognition datasets, namely FineGym and Diving48, show that our proposed method outperforms previous skeleton-based action recognition methods.
Online Distillation and Preferences Fusion for Graph Convolutional Network-Based Sequential Recommendation

Sequential recommendations make an attempt to predict the next item that a user will interact with based on their historical behavior sequence. Recently, considering the relationship learning ability of graph convolutional network (GCN), a number of GCN-based sequence recommendation models have emerged. However, in real-world applications, sparse interactions are common, with early and current short-term preferences playing diverse roles in sequential recommendation. As a result, vanilla GCNs fail to investigate the explicit relationship between these early and current short-term preferences. To address the above limitations, we propose a scheme of Online Distillation and Preferences Fusion for GCN-based sequential recommendation (ODPF). Specifically, our approach performs online distillation among multiple networks to learn item feature representations. To distinguish between early and recent short-term preferences, we divide each sequence into two subsequences and construct two graphs separately. On this basis, a fusion network is introduced to capture more accurate preferences by fusing these two types of preferences. Experimental evaluations conducted on two public datasets demonstrate that our proposed method outperforms recent state-of-the-art methods in terms of recommendation precision.
New advances for quantum‐inspired optimization

Abstract Advances in quantum computing with applications in combinatorial optimization have evolved at an increasing rate in recent years. The quadratic unconstrained binary optimization (QUBO) model is at the center of these developments and has become recognized as an effective alternative method for representing a wide variety of combinatorial optimization problems. Additional momentum has resulted from the arrival of quantum computers and their ability to solve the Ising spin glass problem, another form of the QUBO model. This paper highlights advances in solving QUBO models and extensions to more general polynomial unconstrained binary optimization (PUBO) models as important alternatives to traditional approaches. Computational experience is provided that compares the performance of unique quantum‐inspired metaheuristic solvers—the Next Generation Quantum (NGQ) solver for QUBO models and the NGQ‐PUBO solver for PUBO models—with the performance of CPLEX and the Dwave quantum advantage solver. Extensive results, including experiments with a set of large set partitioning problems representing the largest QUBO models reported in the literature to date, along with maximum diversity and max cut problem sets, disclose that our solvers outperform both CPLEX and Dwave by a wide margin in terms of both computational time and solution quality.
Using the method of image decomposition based on topological features for processing satellite images

The problem of interpretation of spatial data on satellite images is considered in the article. It is proposed to use the decomposition of images by topological features to highlight objects of interest, global and detailed structures on satellite images. The description of the method and the features of its implementation for creating a software product with effective algorithms for processing big data are given. The functionality of the developed software, which includes the classification of objects on satellite images, segmentation, binarization, noise removal is described. It is shown that these algorithms are built on a single theoretical basis in the form of a topological decomposition. Examples of using the program for segmentation and binarization of satellite images from urban neighborhoods are demonstrated.
A study of drama-based art education in Korea through pre-analysis: focus on 3rd graders of primary school students who have limited experienced with drama-based lessons

The purpose of this paper is to study how pre-analysis affects drama-based art programme in South Korea to reach the drama educational goals. In the cognitive, psychodynamic(functional), and affective parts, the learner’s analysis has been analysed through questionnaires, the learner’s situation, and the lesson plan. Also, through the pre-meeting detailed analysis was made such as the learner’s ability and if any children children who required special education. Through the preliminary analysis, it showed that nervousness, prejudice, and burden of students who taking the drama-based art class for the first time are alleviated. It is customised drama-based art education that helps learners’ needs in advance of the lesson.
YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines

Detecting component defects and attaching tiny-scaled foreign objects to the overhead transmission lines are critical to the national grid’s safe operation and power distribution. This urgent task, however, faces challenges, such as the complex working environment and the considerable amount of workforce investment, for which we propose a deep-learning-aided object detection approach, YOLO-CSM, to address the issue. Combined with two attention mechanisms (Swin transformer and CBAM) and an extra detection layer, the proposed model can effectively capture global information and key visual features and promote its ability to identify tiny-scaled defects and distant objects in the visual fields. In order to validate this model, this work consolidates a dataset composed of public images and our field-taken picture samples. The experiment verifies YOLO-CSM as a suitable solution for small and distant object detection that outperforms several well-used algorithms, featuring a 16.3% faster detection speed than YOLOv5 and a 3.3% better detection accuracy than YOLOv7. Finally, this work conducts an interpretability experiment to reveal the similarity between YOLO-CSM’s attention patterns and that of humans, aiming to explain YOLO-CSM’s advantages in detecting small objects and minor defects in the working environments of power transmission lines.
cGAN-Based Garment Line Draft Colorization Using a Garment-Line Dataset

Garment line draft is the basis of clothing design. Automatic or semi-automatic colorization of garment line draft will improve the efficiency of fashion designers and reduce the drawing cost. In this paper, we present a garment line draft colorization method based on cGAN, which can support user interaction by adding scribbles to guide the colorization process. Due to the inadequacy of the garment line drafts, we construct a paired garment-line image dataset for training our colorization model. While existing methods for line art colorization are able to generate plausible colorized results, they tend to suffer from the color bleeding issue. We introduce a region segmentation fusion mechanism to aid colorization frameworks in avoiding color bleeding. The experimental results show that each module in the method can contribute to the final result. In addition, the comparison with the classical methods that our method can avoid large areas of leakage in the background and have cleaner garment details.