A Feature Analysis for Multimodal News Retrieval
Content-based information retrieval is based on the information contained in documents rather than using metadata such as keywords. Most information retrieval methods are either based on text or image. In this paper, we investigate the usefulness of multimodal features for cross-lingual news search in various domains: politics, health, environment, sport, and finance. To this end, we consider five feature types for image and text and compare the performance of the retrieval system using different combinations. Experimental results show that retrieval results can be improved when considering both visual and textual information. In addition, it is observed that among textual features entity overlap outperforms word embeddings, while geolocation embeddings achieve better performance among visual features in the retrieval task.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalNews RetrievalRetrievalWord EmbeddingsSimilar Papers 제목 키워드 기반
ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection
The rapid spread of multimodal fake news poses a serious societal threat, as its evolving nature and reliance on timely factual details challenge existing detection methods. Dynamic Retrieval-Augmented Generation provide…
Fake News DetectionRetrieval-Augmented Multimodal Model for Fake News Detection
In recent years, multimodal multidomain fake news detection has garnered increasing attention. Nevertheless, this direction presents two significant challenges: (1) Failure to Capture Cross-Instance Narrative Consistency…
Fake News DetectionMultiPress: A Multi-Agent Framework for Interpretable Multimodal News Classification
With the growing prevalence of multimodal news content, effective news topic classification demands models capable of jointly understanding and reasoning over heterogeneous data such as text and images. Existing methods …
News ClassificationRASR: Retrieval-Augmented Semantic Reasoning for Fake News Video Detection
Multimodal fake news video detection is a crucial research direction for maintaining the credibility of online information. Existing studies primarily verify content authenticity by constructing multimodal feature fusion…
Domain GeneralizationMultimodal ReasoningGeneral KnowledgeCSA: Data-efficient Mapping of Unimodal Features to Multimodal Features
Multimodal encoders like CLIP excel in tasks such as zero-shot image classification and cross-modal retrieval. However, they require excessive training data. We propose canonical similarity analysis (CSA), which uses two…
Cross-Modal RetrievalGPUimage-classificationImage Classification+1