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Federated Learning (FL), an emerging distributed machine learning framework that enables each client to collaboratively train a global model by sharing local knowledge without disclosing local private data, is vulnerable to backdoor model poisoning ...

Real-time streaming data is generated in high volumes and consumed for statistical and analytical purposes, requiring efficient and effective management by Message Queuing Systems (MQS) that ensure high throughput and low latency. ByteDance relies ...

In recent years, diffusion models have made remarkable strides in text-to-video generation, sparking a quest for enhanced control over video outputs to more accurately reflect user intentions. Traditional efforts predominantly focus on employing ...

Parameter-efficient transfer learning (PETL) has emerged as a flourishing research field for adapting large pre-trained models to downstream tasks, greatly reducing trainable parameters while grappling with memory challenges during fine-tuning. To ...

Medical Visual Question Answering (MedVQA) is designed to answer natural language questions related to medical images. Existing methods largely adopting the cross-modal pre-training and fine-tuning paradigm, face limitations in accuracy due to ...

Reinforcement learning (RL) has shown remarkable success in navigating complex robotic and gaming landscapes. However, achieving such results often requires a substantial number of interaction episodes between the agent and its environment, ...

• The use of HRLfD vastly improves the performance of RL in large and complex tasks.
• We greatly alleviate the inevitable problem of imperfect demonstrations in LfD.
• We propose a novel measure to discriminate negative noise ...

Multimodal Sentiment Analysis (MSA) constitutes a pivotal technology in the realm of multimedia research. The efficacy of MSA models largely hinges on the quality of multimodal fusion. Notably, when conveying information pertinent to specific ...

• Novel multimodal adaptive weight matrix enables accurate sentiment analysis by considering unique contributions of each modality.
• Multimodal attention mechanism addresses over-focusing on intra-modality attention.
• Multiple Softmax ...

Video moment retrieval (VMR) aims to locate corresponding moments in an untrimmed video via a given natural language query. While most existing approaches treat this task as a cross-modal content matching or boundary prediction problem, recent studies ...

Anonymous access is an effective method for preserving privacy in access control. This study assumes that anonymous access control requires both frameworks and policies. Numerous solutions have been proposed for anonymous access at the framework level. In ...

Video captioning aims to briefly describe the content of a video in accurate and fluent natural language, which is a hot research topic in multimedia processing. As a bridge between video and natural language, video captioning is a challenging ...

• A video captioning framework called global semantic enhancement network is proposed.
• It highlights features of informative frames in aggregated video representations.
• It enhances semantic correlations between video and language ...

Video Question Answering (VideoQA) aims to comprehend intricate relationships, actions, and events within video content, as well as the inherent links between objects and scenes, to answer text-based questions accurately. Transferring knowledge from the ...

Text-person retrieval (TPR) is a fine-grained cross-modal retrieval task that aims to find matching person images through detailed text descriptions. Various recent cross-modal pre-trained vision-language based models (e.g., CLIP [12]) have ...

• A novel image-text representation learning network BAERL is proposed.
• It captures both inter- and intra-modality correlations between image regions and words.
• Self-similarity polynomial loss is used to enhance the matching ...

Image-text matching has become a research hotspot in recent years. The key point of image-text matching is to accurately measure the similarity between an image and a sentence. However, most existing methods either focus on the inter-modality ...

To enhance the efficiency of different subsystems within embedded devices, thereby augmenting their capabilities, we incorporate cloud computing architecture within these devices to create an in-device micro-cloud platform. This allows subsystems, which ...

Many virtual machine (VM) allocation methods have been proposed to reduce the number of physical machines (PMs), improve resource utilization for cloud service providers. If VMs are migrated on the same PM, then there will be substantial resource ...

• VM Performance-Aware VMM method of improving users' experience and cloud service providers' benefits.
• Optimizations include: maximizing VM performance, and minimizing the number of active PMs and the total migration cost.
• An ...

• The coreference resolution of geological entities in the geological domain is explored for the first time.
• A novel framework for geological entity coreference resolution based on deep learning methods is proposed.
• A CNN-based multi-...

Coreference resolution of geological entities is an important task in geological information mining. Although the existing generic coreference resolution models can handle geological texts, a dramatic decline in their performance can occur ...

Facial expression recognition (FER) is challenging because the appearance of an expression varies significantly depending on head pose and inter-subject characteristics. With existing techniques, it is often difficult to learn both pose-aware and ...

Reconstructing 3D object from a single image (RGB or depth) is a fundamental problem in visual scene understanding and yet remains challenging due to its ill-posed nature and complexity in real-world scenes. To address those challenges, we adopt a ...

Image-based reconstruction is devoted to recovering the 3D point cloud models of target objects from scene images photographed at different viewpoints, and the existing methods often produce a large number of redundant background points, which ...

Rolling bearing is a very essential component of the industrial machinery. The bearing fault could cause a significant loss. Therefore, it is necessary to perform fault diagnosis and prediction on the bearing. This paper combines Ensemble Empirical Mode ...