Reviewerly/exHarmony huggingface dataset:
An Image Region-Based Image Stitching Algorithm of Smartphones

Image stitching is an important branch of image processing. In order to obtain clear and complete panoramic images, various image mosaic algorithms are proposed. A new region based stitching algorithm is proposed in this paper. The system consists of a rotating table, a controller, a smartphone and a computer. The object is placed on the rotating table, controlled by the controller to rotate slowly, and the smartphone which placed directly in front of it records the video. The images obtained are extracted frame by frame from the captured video. The still image is determined by comparing the similarity between image matrices. The registration regions can be determined by comparing the number of pixels moved of adjacent photos. The new method is helpful to realize artifact free sub region stitching, and then reduce the stitching error.
Multi-Task Feature Decomposition Based Marginal Distribution for Person Search

Person search is a composite task, aiming at locating and identifying a query person from uncropped images. It requires jointly solving Pedestrian Detection and Person Re-identification. One major challenge in person search is the contradictory goals of detection and re-identification. The model has to simultaneously model the universality and specificity of persons. In this paper, we propose a novel parameter-free approach called Feature Decomposition Person Search (FDPS) to separate various tasks. FDPS decomposes the ROI feature map to extract sub-features based on the marginal distribution for different tasks. Also, we find that the Online Instance Match loss pays imbalanced attention to positive and negative categories. We present a Balance Online Instance Match (BOIM) loss to enhance the contribution of negative categories during training. Our method achieves the state-of-the-art performance in one-step methods on two prevailing benchmarks, with high efficiency.
Double-net DDPG with the Optimal Action Selection Mechanism

Recently, the deep reinforcement learning method based on actor-critic has a competent performance in continuous action control tasks, such as the proposed deep deterministic policy gradient (DDPG) algorithm. However, this algorithm also has its shortcomings in applications. For example, the actor network and the critic network are closely dependent, and the critic network is always prone to overestimation. Those may lead to poor updates of the policy. Different from the conventional double networks taking the smallest operation, different target actor networks are applied to generate actions when the critic network is updated. Considering the structure of the double networks, a better action is preferred, which is conducive to speeding up the network convergence. In this paper, the Double-Net DDPG algorithm with double actor networks and double critic networks is proposed, with the optimal action selection mechanism. It reduces the network dependency in DDPG. The result shows the proposed algorithm has improved the original algorithm and achieves excellent performance in the continuous action control task.
Generalizing Factorization of Gans by Characterizing Convolutional Layers

Existing unsupervised disentanglement methods in latent space of the Generative Adversarial Networks (GANs) rely on the analysis and decomposition of pre-trained weight matrix. However, they only consider the weight matrix of the fully connected layers, ignoring the convolutional layers which are indispensable for image processing in modern generative models. This results in the learned latent semantics lack inter-pretability, which is unacceptable for image editing tasks. In this paper, we propose a more generalized closed-form factor-ization of latent semantics in GANs, which takes the convolutionallayers into consideration when searching for the under-lying variation factors. Our method can be applied to a wide range of deep generators with just a few lines of code. Exten-sive experiments on multiple GAN models trained on various datasets show that our approach is capable of not only finding semantically meaningful dimensions, but also maintaining the consistency and interpretability of image content.
Interactive Multimedia Development Using Articulate Storyline Application on Thermochemistry Subject Matter

The development of interactive multimedia by Articulate Storyline application aims to develop an innovative and independent learning media for 11 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> grade students at Senior High School (SMA) on Thermochemistry subject matter. This research was designed using Research & Development (R&D) design with Plomp development model. Expert assessments (validation sheets) and user (teachers and students) responses are data collection technique in this research. The instruments in the form a Likert attitude measurement scales 1-4. Analysis of the research data was carried out descriptively and quantitatively with mathematically using percentages. From the data analysis, known from media experts based on software engineering aspects (95.31%) and visual communication (display) aspects (96.67%) with valid criteria. And by material experts based on aspects of material substance (95%), presentation of material aspects (95%) and language aspects (100%) with valid criteria. The user response from teachers obtained of 95% and 93.42% from students, both with very good criteria. Obtaining a positive assessment and response means that this interactive multimedia product can be valid and suitable for use in the learning process as an innovative learning media.
EDSF: Fast and Accurate Ellipse Detection via Disjoint-Set Forest

We present a novel yet effective method for detecting elliptical primitives in cluttered, occluded images, which has versatile applications in computer vision and multimedia processing fields. We begin by the fast extraction of smooth arcs from the edge map, followed by the construction of a directed graph and a disjoint-set forest, whereby the arc relationships are effectively encoded to enhance the arc grouping process. Compared with representative approaches such as the depth-first search, the disjoint-set forest enables complete grouping of arcs to generate candidate ellipses. Moreover, it merely has linear memory complexity and constant access time, hence guarantees fast detection. To boost precision and remove false positives, we propose to project the candidate ellipses onto the original image, to align the gradients of ellipses and the image pixels. We also vectorize the elliptical parameters to depress duplicated candidates. We perform extensive experiments on both synthetic and challenging real-world datasets, to show that our detector is accurate and efficient, as well as versatile in many practical tasks. The source code and datasets are available at https://github.com/xiaowuga/EDSF.
Salient Object Detection via Bilateral Feature Fusion and Score Sorting Attention Mechanism

Deep learning based salient object detection methods have recently received significant attention. However, current methods still suffer from shortcomings such as informative background information being ignored which a significant problem for image saliency understanding. Additionally, it is also a challenge to suppress the noisy features in the network. By analyzing the difference between high-level and low-level features from ResNet-50, we utilize a Bilateral Feature Fusion (BFF) module to deal with the problem caused by ignoring informative background information. Benefitting from the BFF module, our proposed network can capture more meaningful foreground and background cues, which helps to get a more accurate saliency map. Moreover, we adopt a Score Sorting Attention (SSA) module which suppresses noisy and irrelevant features. Experimental results on five benchmark datasets demonstrate that our proposed method performs better than other state-of-the-art methods. The ablation studies also prove our contributions.
Projective Transformation Based Virtual Reference Frame Generation Scheme for Video Coding in Moving Camera

In this paper, we propose a projective transformation based virtual reference frame generation scheme for video coding in moving camera scenarios. The generated virtual reference frame can provide high qualitative reference in the process of removing temporal redundancy and improve the compression performance of the encoder. We also propose a depth estimation algorithm, which combines optical flow with the triangulation method from Simultaneous Localization and Mapping (SLAM), to acquire the accurate pixel-level image depth for projective transformation. To further improve the compression efficiency, we modify the syntax of High Efficiency Video Coding (HEVC) to support the coding of camera parameters and new motion vector scaling method. Experimental results show that compared with HEVC reference software HM16.7, our method has a Bjøntegaard Delta bit rate (BD-rate) gain of 7.80% and a encoding time gain of 23.9% in spite of increasing decoding complexity.
A<sup>2</sup>SC: Adversarial Attack on Subspace Clustering

Many studies demonstrate supervised learning techniques are vulnerable to adversarial examples. However, adversarial threats in unsupervised learning have not drawn sufficient scholarly attention. In this paper, we formally address the unexplored adversarial attacks in the equally, if not more, important unsupervised clustering field and propose the concept of adversarial set. To illustrate the basic idea, we design an exemplary adversarial space-mapping attack algorithm to confuse subspace clustering, one of the mainstream branches of unsupervised clustering. It maps a sample into one wrong class by moving it towards the closest point on the linear subspace of the target class, i.e. along the normal of the closest point. The simple single-step algorithm is powerful to craft the adversarial set where the samples can be wrongly clustered, even into targeted labels. The adversarial set has the merit of transferability among subspace clustering schemes. Empirical results verify the effectiveness and transferability of our algorithm.
Hardware Design of Combinational 128-bit Camellia Symmetric Cipher using 0.18µm Technology

Camellia is another symmetric key block cipher with a 128-bit block size and key sizes of 128, 192, and 256 bits. As the hardware version of cipher implementation is getting popular, the Camellia hardware design should be constantly improved in terms of performance. The key aim of this research is to design and implement the improved version of Camellia cipher using custom-based approach based on 0.18µm technology. The new hardware Camellia design is synthesized using timing, area and power constraints to achieve less area, less power and high-speed design. Finally the results are been compared and analyzed with previous implementation in terms of performance parameters. The design is described using Verilog HDL and been verified using functional simulation from the Modelsim-Intel FPGA, while Synopsys had been utilized for synthesis process. Based on the generated synthesis reports, the total area consumed by the design on ASIC platform is 1294.11-unit area with a total cell area of 1176.25-unit area, while the total power consumption is 0.1245mW, and an increased speed of more than 80% as compared to previous design. In conclusion, this design had achieved better performance compared to previous design.
Effcient Shift Network in Denoising-Friendly Space for Real Noise Removal

Recently, following the success of neural networks, image denoising has achieved great improvements. However, it is challenging to construct an efficient denoising model with excellent performance and less computation. In this paper, we propose an extremely lightweight framework to remove real image noise in denoising-friendly space. Specif-ically, we apply the wavelet transform to project the noisy image and feature maps into low and high frequency domain, which decouples the noise information from the clean ones to a certain extent, thereby reducing the difficulty of denoising task. In addition, we further introduce a lightweight op-erator called Grouped Shift Module (GSM) into our denoising network, hence much heavy computation can be saved. Experimental results on the current benchmark demonstrate that our Wavelet Shift Denoising Network (WSNet) even achieves PSNR 39.28 dB with only 3G FLOPs on the SIDD benchmark. Our source code and models are available at https://github.com/HIK-DLSlimIWSNet.
An Experimental Comparison between Low-Resource Semi-Supervised and High-Resource Supervised Automatic Speech Recognition Models

Automatic speech recognition (ASR) is an important module in many multimedia applications. Recently, semi-supervised ASR has attracted increasing attention, which can be classified into self-supervised learning and self-training. It was shown that the combination of the two methods is beneficial for ASR on open datasets, e.g., LibriSpeech. However, it is still not completely clear how the semi-supervised model behaves on more challenging industrial datasets compared with supervised approaches using high-resource labeled data. In this paper, we therefore present an experimental study on the combination of self-supervised learning (e.g., wav2vec 2.0) and self-training (e.g., noisy student) in a low-resource industrial setting. The in-domain pre-trained and fine-tuned wav2vec 2.0 model is utilized to teach a baseline VGG-Transformer by pseudo-labeling in an industrial setting. Results reveal that without extra language models the combined semi-supervised acoustic model using much less labeled data can perform as well as the supervised counterpart, which would be rather beneficial for the application of semi-supervised ASR models in low-resource scenarios.
Dast-Net: Depth-Aware Spatio-Temporal Network for Video Deblurring

Video deblurring is a challenging task due to inevitable blurs caused by depth variation, object motion, and camera shake. Although several video deblurring methods resort to depth maps, they rarely produce visually appealing results since the information in the depth maps is used insufficiently. To address this issue, we propose a Depth-Aware Modulated (DAM) block for efficiently utilizing the depth map characteristics, in which the intensity and variation of depth are exploited according to the depth map value and edges. Based on the DAM block, we develop the Depth-Aware Spatio-Temporal Network (DAST-Net) tailored for video deblurring. Particularly, the Depth-Aware Temporal Alignment module uses the depth cues to guide the alignment of adjacent frames. The Depth-Modulated Spatial Fusion module then warps the aligned frames to maintain spatial invariance with the aligned features. The warped depth features are more effective in video deblurring, since they allow for the aggregation of multiple frames. Extensive quantitative and qualitative evaluations demonstrate that the proposed DAST-Net outperforms other state-of-the-art methods.
Research on Internet of Things Impacting Internal Control of China's Financial Report

As one of the future development trends, the Internet of Things will determine the future direction of the world and human society in all aspects. Since the introduction of the wisdom of the Earth, the perception of China and other strategies, countries around the world have also carried out the planning and layout of the Internet of Things, and raised the construction of the Internet of Things to the national strategy. The Internet of Things has set off a third wave of information revolutions following computers and the Internet around the world. In the Internet of Things environment, China's financial reporting internal control research has become an important research topic. The article discusses the impact of the Internet of Things on the internal control of financial reporting, and develops a scientific and effective practical strategy on how to optimize the control effect. It is of great practical significance to meet the characteristics of the times, stimulate the comprehensive superior value of financial information systems, and improve the internal control and management level of enterprises.
Reliable Self-Supervised Information Mining for Deep Subspace Clustering

Deep subspace clustering has achieved remarkable performance in unsupervised clustering tasks. The self-supervised approach is further introduced to learn more discriminative representation for enhancing clustering performance. Despite the significant improvement of clustering performance by exploiting self-supervision information, these approaches heavily depend on the high quality of pseudo-label from the current clustering result and this will inevitably degrade the clustering performance when the obtained pseudo-labels are incorrect. To solve this issue, we develop a robust self-supervised deep subspace clustering approach by exploiting the reliable self-supervised information during training. The proposed method is involved in two key steps: a diffusion processing step is developed to improve self-expressiveness matrix such that more accurate clustering result (pseudo-labels) can be obtained. More importantly, we further propose to estimate and exploit the reliability of the assigned pseudo-label for each sample to alleviate the negative impact of incorrect pseudo-labels, such that the unreliable self-supervision can be further alleviated. Experimental studies on several benchmark datasets validate the effectiveness of our approach in terms of refining the self-supervised information. The source code of the proposed method is available at the https://github.com/stuljj/RSDSC.git.
Mask-Guided Self-Distillation For Visual Tracking

Recently, Siamese-based visual trackers has been dramatically improved on performance, however, excessive parameters of tracking networks make models seriously hinder the practical deployment on edge devices. In this work, we present mask-guided self-distillation(MGSD) to compress the models of Siamese-based visual trackers, which enables Siamese-based visual trackers to capture crucial knowledge for effecting the performance of tracking. Specifically, MGSD consists of mask-guided semantic features self-distillation and decoupled tracking-head self-distillation, which discards the redundant convolution parameters in feature-dependency and task-dependency. It is worth noting that the proposed method compress exponentially the models of Siamese-based trackers under the condition of keeping even improving the performance of tracking. Extensive comprehensive experiments on tracking benchmarks including OTB2015, UAV123, LaSOT demonstrate that our proposed method can compress model size of Siamese-based visual trackers to 50% while achieving state-of-the-art performance. Our source code is available at: https://github.com/xl0312/MGSD.
A Chinese Named Entity Recognition Method Based on Fusion of Character and Word Features

Named entity recognition is the upstream task in natural language processing tasks and is the basis for carrying out other downstream tasks. To enhance the effectiveness of the model for named entity recognition, a character vector with semantic features is obtained using BERT as the underlying encoder, followed by contextual features of the text sequence via BILISTM. In the Chinese named entity recognition task, both words and characters are equally important to the text, so a FLAT network is embedded to fuse word and character features. The network uses clever relative position encoding to preserve the location information of the input token, and generates potential word and character vectors to be added to the model for training. Experimental results show an increase in F1 values of 1.86% and 1.47% on the Resume and self-annotated news corpus datasets, respectively.
Synthetic-to-Real Generalization for Semantic Segmentation

The discrepancy between synthetic and real data is crucial to the performance of domain generalization for semantic segmentation. Since real data is not always accessible, a popular line of approaches is to enhance the diversity of synthetic data via either complex adversarial generation or unstable stylization. However, the internal structure of the synthetic image is often neglected. To largely explore useful information in synthetic data, we observe that, although objects of the same category have different texture patterns between domains, their shapes are quite similar. Based on this observation, we argue that focusing on structural information and alleviating texture dependence are effective ways to improve generalization capability. In this work, we propose an end-to-end network, which explicitly constrains the network to learn shapes and spatial knowledge, and implicitly relieves the texture reliance of the network. Extensive experiments verify the effectiveness of our proposed method and demonstrate its clear advantages over other competitors.
Performance Analyses of Discrete-Time RNN for Solving Discrete-Form Time-Variant Matrix Inversion with Different Selection Parameters

Neural network could be considered as a basic artificial intelligence methods. In this paper, we explore a lot of researches on performance analyses of discrete-time recurrent neural network (DT-RNN) model. For solving the discrete-form time-variant matrix inversion (DF-TV-MI), continuous-time recurrent neural network (CT-RNN) model is presented firstly. Then, an inspirational method named general-four-instant discretization formula (GFI discretization formula) to discrete the CT-RNN model, and we obtain a new DT-RNN model. Finally, we show the performance analyses of DT-RNN model solving for DF-TV-MI with different selection parameters.
Tokenizing Features for Fast Video Object Segmentation

This paper investigates how to take full advantage of the tem-poral and spatial information in videos with minimal compu-tational cost in the semi-supervised video object segmentation (VOS) task. Current state-of-the-art methods have achieved remarkable performance by matching features of the current frame with those of past frames to propagate the past segmen-tation masks to the current. However, the inference speeds of such matching-based methods are limited due to the tremen-dous amount of computation on pixel-to-pixel matching. To address this problem, we propose a fast matching mechanism for VOS that extracts essential object information as a handful of token vectors for matching. By extracting succinct but suf-ficient information from the pixel-wise features, we develop a fast VOS model which achieves competitive segmentation performance (81.6% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$J$</tex> & <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F$</tex> on DAVIS-2017), maintaining a high inference speed (FPS = 42.1).
An Attention-Guided Two-Stream Convolutional Neural Network for Few-Shot Learning

Learning unlabeled samples from unseen categories given limited labeled data is a challenging problem. Existing few-shot learning methods fail to generate satisfactory feature representations due to tackling the informative and interference information without distinction. In this paper, we propose an attention-guided two-stream convolutional neural network (AGTSNet), which highlights the salient and discriminative features of the main object while alleviating the background interference to address this indiscriminate treatment. Comprehensive experiments are conducted on few-shot image classification with four standard benchmark datasets to demonstrate the effectiveness of our method.