Text Is MASS: Stochastic Text Modeling for Text-Video Retrieval
Enriching text embedding with flexibility and resilience through stochastic modeling enhances text-video retrieval performance.
D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications
提案されたD2-JSCCフレームワークは、SemComの画像伝送問題に対処し、デジタルソースとチャネルコーディングを組み合わせてエンドツーエンドの歪みを最小限に抑えます。
Leveraging Large Language Models for Ontology and Knowledge Graph Construction
Large Language Models (LLMs) offer a semi-automatic approach to constructing Knowledge Graphs (KGs) by reducing human effort and time in the process.
ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data
ACORN presents a performant and predicate-agnostic approach for hybrid search, utilizing Hierarchical Navigable Small Worlds (HNSW) to achieve state-of-the-art performance on diverse datasets.
GoLLIE: Annotation Guidelines for Zero-Shot Information Extraction
GoLLIE improves zero-shot information extraction by following annotation guidelines, outperforming previous attempts.
Impact of Large Language Models on Recommender Systems: A Comprehensive Review
Large Language Models (LLMs) are reshaping recommender systems by leveraging their unique language comprehension abilities, revolutionizing recommendation tasks and enhancing user experiences.
Text2Pic Swift: Enhancing Long-Text to Image Retrieval for Large-Scale Libraries
The author introduces Text2Pic Swift, a framework designed for efficient and robust retrieval of images from extensive textual descriptions in large datasets. The framework employs Entity-based Ranking and Summary-based Re-ranking stages, along with a novel Decoupling-BEiT-3 encoder, to improve computational efficiency and achieve better performance than current MLLMs.
Retrieval-based Full-length Wikipedia Generation for Emergent Events: Challenges and Solutions
The author addresses the challenges of generating full-length Wikipedia articles for emergent events and proposes a retrieval-based approach to overcome these obstacles.
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