#dataset-construction News & Analysis

5 articles tagged with #dataset-construction. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Jun 237/10

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XmoPipe: A Pipeline for Large-Scale In-the-Wild Human Motion Dataset Construction

XmoPipe is a scalable pipeline that constructs large-scale human motion datasets by extracting 3D body and facial motion from unconstrained online videos, combined with automated textual descriptions. The system demonstrates that motion models trained on this in-the-wild data achieve performance comparable to traditional marker-based motion capture datasets while offering superior scalability and diversity.

AIBullisharXiv – CS AI · Jun 97/10

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FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction

FineGen is a VLM-based multi-agent framework that automatically constructs vision-language datasets by generating hard negative samples through a Generation-Verification-Correction pipeline. The resulting FineGen-100K dataset contains 147,000+ attribute-specific hard negatives and demonstrates a 14.4% accuracy improvement on fine-grained object detection benchmarks, addressing a critical gap in existing datasets.

AINeutralarXiv – CS AI · Jun 86/10

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Never Seen Before: Benchmarking Genuine Zero-Shot Composed Image Retrieval with Consistent Video-Sourced Datasets

Researchers introduce ZeroSight, a new benchmark for Zero-Shot Composed Image Retrieval that addresses critical flaws in existing datasets by using video-sourced data published after CLIP's training cutoff and proposing SC4CIR, a training-free method that reveals current ZS-CIR performance metrics significantly overestimate actual model capabilities.

AINeutralarXiv – CS AI · Apr 135/10

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MuTSE: A Human-in-the-Loop Multi-use Text Simplification Evaluator

MuTSE is an interactive web application designed to evaluate Large Language Model outputs for text simplification tasks across multiple prompting strategies and proficiency levels. The tool addresses a methodological gap in NLP research by providing researchers and educators with a structured, visual framework for comparing prompt-model combinations in real-time.

AINeutralApple Machine Learning · Feb 245/103

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Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining

Researchers investigate whether using a single HTML-to-text extractor for web-scale LLM pretraining datasets leads to suboptimal data utilization. The study reveals that different extractors can result in substantially different pages surviving filtering pipelines, despite similar model performance on standard language tasks.