Search
Search Results
-
Object semantic analysis for image captioning
Although existing image captioning models can produce sentences through attention mechanisms and recurrent neural networks, it is difficult to...
-
Structured Encoding Based on Semantic Disambiguation for Video Captioning
Video captioning, which aims to automatically generate video captions, has gained significant attention due to its wide range of applications in...
-
Semantic-specific multimodal relation learning for sentiment analysis
Multimodal sentiment analysis (MSA) seeks to understand human affection by leveraging signals from multiple modalities. A core challenge in MSA is...
-
Motion2language, unsupervised learning of synchronized semantic motion segmentation
In this paper, we investigate building a sequence to sequence architecture for motion-to-language translation and synchronization. The aim is to...
-
Semantic enhancement and multi-level alignment network for cross-modal retrieval
Cross-modal retrieval aims to address heterogeneity and cross-modal semantic associations between multimedia data of different modalities. Image-text...
-
A distributed framework for large-scale semantic trajectory similarity join
The similarity join is a common yet expensive operator for large-scale semantic trajectories analytics. In this paper, we propose
DFST , an efficient...
-
A real-time semantic based approach for modeling and reasoning in Industry 4.0
In the rapidly evolving landscape of Industry 4.0, the transformation of manufacturing processes is driven by the seamless integration and...
-
Model division multiple access for semantic communications
In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally...
-
Semantic-wise guidance for efficient multimodal emotion recognition with missing modalities
Emotions play an important role in human–computer interaction. Multimodal emotion recognition combines feature information from different modalities...
-
Semantic Analysis
This chapter studies semantic analysis, one of the core concepts for learning NLP. There are two basic schemes of semantic analysis: lexical and...
-
Semantic difference-based feature extraction technique for fake news detection
The rise of fake news presents a critical challenge to societal stability, emphasizing the urgent need for efficient detection systems. This study...
-
Dataset and semantic based-approach for image sonification
This paper presents an image-audio dataset and a mid-level image sonification system that strives to help visually impaired users understand the...
-
SportsTables: A New Corpus for Semantic Type Detection (Extended Version)
Table corpora such as VizNet or TURL which contain annotated semantic types per column are important to build machine learning models for the task of...
-
MGSGA: Multi-grained and Semantic-Guided Alignment for Text-Video Retrieval
In the text-video retrieval task, the objective is to calculate the similarity between a text and a video, and rank the relevant candidates higher....
-
Improved content recommendation algorithm integrating semantic information
Content-based recommendation technology is widely used in the field of e-commerce and education because of its intuitive and easy to explain...
-
Semantic-alignment transformer and adversary hashing for cross-modal retrieval
Deep Cross-Modal Hashing (DCMH) has garnered significant attention in the field of cross-modal retrieval due to its advantages such as high...
-
Semantic- and relation-based graph neural network for knowledge graph completion
Knowledge graph completion (KGC) refines missing entities, relationships, or attributes from a knowledge graph, which is significant for referral...
-
Identifying the driving factors of word co-occurrence: a perspective of semantic relations
This study aims to investigate and identify the driving factors of word co-occurrence from the perspective of semantic relations between frequently...
-
Enhancing knowledge graph embedding with structure and semantic features
Knowledge graph embedding converts knowledge graphs based on symbolic representations into low-dimensional vectors. Effective knowledge graph...
-
PPNet : pooling position attention network for semantic segmentation
Semantic segmentation with attention module has made great progress in many computer vision tasks. However, attention modules ignore some boundary...
