The current audio single-mode self-supervised classification mainly adopts a strategy based on audio spectrum reconstruction. Overall, its self-supervised approach is relatively single and cannot fully mine key semantic information in the time and fr...
Although the means of catching remote sensing images are becoming more effective and more abundant, the samples that can be collected in some specific environments can be quite scarce. When there are limited labeled samples, the methods for analyzing...
Three-dimensional human pose and shape estimation is an important problem in the computer vision community, with numerous applications such as augmented reality, virtual reality, human computer interaction, and so on. However, training accurate 3D hu...
The accurate estimation of a 3D human pose is of great importance in many fields, such as human–computer interaction, motion recognition and automatic driving. In view of the difficulty of obtaining 3D ground truth labels for a dataset of 3D po...
Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question for medical image classification, with a particular focus on one of t...
Presently, the harm to human health created by air pollution has greatly drawn public attention, in particular, vehicle emissions including nitrogen oxides as well as particulate matter. How to predict air quality, e.g., pollutant concentration, effi...
Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep learning methods driven by massive data cannot achieve the intended expectations. Semi-supervised and self-supervised algorithms have advantages in coping with...
In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defin...
Collaborative filtering (CF) based on graph neural networks (GNN) can capture higher-order relationships between nodes, which in turn improves recommendation performance. Although effective, GNN-based methods still face the challenges of sparsity and...
Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream...
Self-supervised learning approaches have seen success transferring between similar medical imaging datasets, however there has been no large scale attempt to compare the transferability of self-supervised models against each other on medical images....
Automated radiology report generation has become a prominent research topic in medical multimodal learning. However, most existing approaches primarily focus on single-image interpretation and rarely address the task of tracking disease progression a...
We propose the implementation of transfer learning from natural images to audio-based images using self-supervised learning schemes. Through self-supervised learning, convolutional neural networks (CNNs) can learn the general representation of natura...
To alleviate the impact of insufficient labels in less-labeled classification problems, self-supervised learning improves the performance of graph neural networks (GNNs) by focusing on the information of unlabeled nodes. However, none of the existing...
Plant diseases have been one of the most threatening scenarios to farmers. Although most plant diseases can be identified by observing leaves, it often requires human expertise. The recent improvements in computer vision have led to introduce disease...
Due to the low cost and easy deployment, self-supervised depth completion has been widely studied in recent years. In this work, a self-supervised depth completion method is designed based on multi-modal spatio-temporal consistency (MSC). The self-su...
This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification. Two state-of-the-art self-supervised learning me...
With the increasing visual realism of computer-graphics (CG) images generated by advanced rendering engines, the distinction between CG images and natural images (NIs) has become an important research problem in the image forensics community. Previou...
Transfer learning has been shown to be an effective method for achieving high-performance models when applying deep learning to remote sensing data. Recent research has demonstrated that representations learned through self-supervision transfer bette...
Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when con...
Enhancing image quality provides more interpretability for both human beings and machines. Traditional image enhancement techniques work well for specific uses, but they struggle with images taken in extreme conditions, such as varied distortions, no...
Face Parsing aims to partition the face into different semantic parts, which can be applied into many downstream tasks, e.g., face mask up, face swapping, and face animation. With the popularity of cameras, it is easier to acquire facial images. Howe...
Seismic deconvolution is a useful tool in seismic data processing. Classical non-machine learning deconvolution methods usually apply quite a few constraints to both wavelet inversion and reflectivity inversion. Supervised machine learning deconvolut...
Contrastive-based clustering models usually rely on a large number of negative pairs to capture uniform representations, which requires a large batch size and high computational complexity. In contrast, some self-supervised methods perform non-contra...
Solar radio observation is an important way to study the Sun. Solar radio bursts contain important information about solar activity. Therefore, real-time automatic detection and classification of solar radio bursts are of great value for subsequent s...
Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, the...
Physical activities, according to the embodied cognition theory, are an important manifestation of cognitive functions. As a result, in this paper, the Activate Test of Embodied Cognition (ATEC) system is proposed to assess various cognitive measures...
The rise of graph neural networks has greatly contributed to the development of recommendation systems, and self-supervised learning has emerged as one of the most important approaches to address sparse interaction data. However, existing methods mos...
In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised me...
Self-supervised learning continues to drive advancements in machine learning. However, the absence of unified computational processes for benchmarking and evaluation remains a challenge. This study conducts a comprehensive analysis of state-of-the-ar...
We present a new method of self-supervised learning and knowledge distillation based on multi-views and multi-representations (MV–MR). MV–MR is based on the maximization of dependence between learnable embeddings from augmented and non-au...
Text geolocation prediction aims to infer the geographic location of text with text semantics, serving as a fundamental task for various geographic applications. As the mainstream approach, the deep learning-based methods follow the supervised learni...
Accurate traffic prediction is pivotal when constructing intelligent cities to enhance urban mobility and to efficiently manage traffic flows. Traditional deep learning-based traffic prediction models primarily focus on capturing spatial and temporal...
Due to the existence of low-textured areas in indoor scenes, some self-supervised depth estimation methods have specifically designed sparse photometric consistency losses and geometry-based losses. However, some of the loss terms cannot supervise al...
For the task of monocular depth estimation, self-supervised learning supervises training by calculating the pixel difference between the target image and the warped reference image, obtaining results comparable to those with full supervision. However...
The success of image classification depends on copious annotated images for training. Annotating histopathology images is costly and laborious. Although several successful self-supervised representation learning approaches have been introduced, they...
Background/Objectives: Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality that provides high-fidelity soft-tissue contrast without ionizing radiation. However, acquiring high-resolution MRI scans is time-consuming, necess...
To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size...
AIS, the global ship identification standard, is vulnerable to outages, coverage gaps, and deliberate deactivation, highlighting the need for independent ship identification methods. Optical imaging satellites offer a global, non-compliance-dependent...
Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich,...
Recently, self-supervised learning methods have been shown to be very powerful and efficient for yielding robust representation learning by maximizing the similarity across different augmented views in embedding vector space. However, the main challe...
Probe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles. Recent advancements...
As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks have been successfully used to model interaction an...
Automatic ship detection provides an essential function towards maritime domain awareness for security or economic monitoring purposes. This work presents an approach for training a deep learning ship detector in Sentinel-2 multi-spectral images with...
Deep learning methods have become an integral part of computer vision and machine learning research by providing significant improvement performed in many tasks such as classification, regression, and detection. These gains have been also observed in...
Due to the complementary nature of graph neural networks and structured data in recommendations, recommendation systems using graph neural network techniques have become mainstream. However, there are still problems, such as sparse supervised signals...
Despite the in-depth understanding of the synthetic aperture-radar (SAR) speckle and its characteristics, despeckling remains an open issue far from being solved. Deep-learning methods with supervised training have made great progress. However, relia...
The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in o...
To successfully train a deep neural network, a large amount of human-labeled data is required. Unfortunately, in many areas, collecting and labeling data is a difficult and tedious task. Several ways have been developed to mitigate the problem associ...
Deep learning (DL), a hierarchical feature extraction method, has garnered increasing attention in the remote sensing community. Recently, self-supervised learning (SSL) methods in DL have gained wide recognition due to their ability to mitigate the...
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