Zobrazeno 1 - 10
of 633
pro vyhledávání: '"WU, Q. M. JONATHAN"'
Unsupervised domain adaptation (UDA) aims to mitigate the domain shift issue, where the distribution of training (source) data differs from that of testing (target) data. Many models have been developed to tackle this problem, and recently vision tra
Externí odkaz:
http://arxiv.org/abs/2407.21311
Autor:
Liu, Shuaixin, Li, Kunqian, Ding, Yilin, Xu, Kuangwei, Jiang, Qianli, Wu, Q. M. Jonathan, Song, Dalei
We introduce a novel vision-based framework for in-situ trunk identification and length measurement of sea cucumbers, which plays a crucial role in the monitoring of marine ranching resources and mechanized harvesting. To model sea cucumber trunk cur
Externí odkaz:
http://arxiv.org/abs/2406.13951
This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data
Externí odkaz:
http://arxiv.org/abs/2406.03398
High precision, lightweight, and real-time responsiveness are three essential requirements for implementing autonomous driving. In this study, we incorporate A-YOLOM, an adaptive, real-time, and lightweight multi-task model designed to concurrently a
Externí odkaz:
http://arxiv.org/abs/2310.01641
Magnetic resonance (MR) and computer tomography (CT) imaging are valuable tools for diagnosing diseases and planning treatment. However, limitations such as radiation exposure and cost can restrict access to certain imaging modalities. To address thi
Externí odkaz:
http://arxiv.org/abs/2306.01562
Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To address these issues, this paper proposes a new interpretable SSL model
Externí odkaz:
http://arxiv.org/abs/2305.14373
Generally, current image manipulation detection models are simply built on manipulation traces. However, we argue that those models achieve sub-optimal detection performance as it tends to: 1) distinguish the manipulation traces from a lot of noisy i
Externí odkaz:
http://arxiv.org/abs/2211.10922
Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to so
Externí odkaz:
http://arxiv.org/abs/2211.01293
Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it
Externí odkaz:
http://arxiv.org/abs/2203.06598
The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images
Externí odkaz:
http://arxiv.org/abs/2201.05730