In scenarios like privacy protection or large-scale data transmission, data-free knowledge distillation (DFKD) methods are proposed to learn Knowledge Distillation (KD) when data is not accessible. They generate pseudo samples by extracting the ...

• The proposed novel network for medical image segmentation outperforms some SOTA models on four datasets.
• Dual-branch encoder effectively captures the local features and models long-distance dependencies.
• Two-stage fusion strategy ...

Computer-based biomedical image segmentation plays a crucial role in planning of assisted diagnostics and therapy. However, due to the variable size and irregular shape of the segmentation target, it is still a challenge ...

Leak location using cross-correlation of acoustic signals collected by acceleration sensors is easily disturbed by the environmental noises resulting in inaccurate identification of its location, especially at low SNR. Aiming at this problem, an ...

Recent research efforts on Few-Shot Learning (FSL) have achieved extensive progress. However, the existing efforts primarily focus on the transductive setting of FSL, which is heavily challenged by the limited quantity of the unlabeled query set. ...

• We highlight the significance of the inductive Few-Shot Learning in the real-world settings.
• The existing inductive FSL methods usually ignore the relations between sample-level and class-level representations.
• The proposed HKPD ...

Data-free knowledge distillation (DFKD) is a widely-used strategy for Knowledge Distillation (KD) whose training data is not available. It trains a lightweight student model with the aid of a large pretrained teacher model without any ...

Person re-identification aims to retrieve specific pedestrians from different cameras and scenes, in which extracting robust and discriminative features is crucial for this task. To explore the potential interactions among images and learn more ...

• A Transformer-based network for multi-scale information interaction.
• Constructing context-aware features containing global information and detailed features with pyramidal attention.
• Adaptive penalty margin for tighter custom ...

Digital Twins (DTs) can generate digital replicas for mobile networks (MNs) that accurately reflect the state of MN. Machine learning (ML) models trained in DT for MN (DTMN) virtual environments can be more robustly implemented in MN. This can avoid the ...

Empowered by labeled datasets, supervised pre-training based transfer learning (SPTL) has made significant advances for image classification applications. However, due to privacy-preserving protocol and unaccessible annotation, it emerges as a novel ...

With the rapid development of information technology, image and text data have increased dramatically. Image and text matching techniques enable computers to understand information from both visual and text modalities and match them based on semantic ...

More robust intelligent transportation systems including autonomous driving systems are in full flourish with the revolution of deep learning and the 6G wireless communication network. Vehicle Re-Identification, an indispensable branch of the intelligent ...

With the emergence and development of deep generative models, such as the variational auto-encoders (VAEs), the research on topic modeling successfully extends to a new area: neural topic modeling, which aims to learn disentangled ...

• We propose the contrastive disentangled neural topic model based on topic embedding.

Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Recent methods solve this problem by predicting the adjacency relations of detected cell boxes, or learning to generate the corresponding markup ...

Person re-identification (ReID) aims to match the specific pedestrians in the public environment with cross-domain cameras. Posture change, occlusion, and viewpoint change complicate ReID. Representation learning and metric learning have been ...

Existing machine learning technology lacks effective support for practical drone devices in target recognition. The Clustered Federated Learning Algorithm (C-FLA) was proposed to enhance the joint training in drone groups. The algorithm considered the ...

Vehicle re-identification aims to retrieve the target vehicle from the image gallery quickly and accurately. Vehicle re-identification with deep learning has achieved considerable performance. However, most popular methods need construct complex ...

In this paper, we study the cross-modal image retrieval, where the inputs contain a source image plus some text that describes certain modifications to this image and the desired image. Prior work usually uses a three-stage strategy to ...

Person re-identification aims to identify the same person across different cameras, which is widely applied in the intelligent monitoring field. The research of TriHard loss has been verified to improve performance efficiently in the person re-...

Person re-identification (ReID) aims to associate the person with the given identity across different cameras, which has wide application in the field of intelligent video. In this work, an efficient method is proposed to improve ReID performance. ...

The relevance vector machine (RVM) is considered a robust machine learning method and its superior performance has been confirmed through many successful engineering applications. To improve the performance of the RVM model, three single kernel ...

• A vehicle attribute transformer for vehicle re-identification is proposed, which can aggregate the attributes of vehicle model, color and viewpoint ...

With the continuous development of intelligent transportation systems, vehicle-related fields have emerged a research boom in detection, tracking, and retrieval. Vehicle re-identification aims to judge whether a specific vehicle ...