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This paper introduces SemanticSeg, a large-scale dataset for semantic segmentation of long texts, and block distillation, a training framework that enables block attention models to approach full-attention performance, improving KV cache reuse in RAG and long-context scenarios.
This paper proposes a semantic feature segmentation framework for predictive maintenance that decomposes monitoring signals into canonical and residual components to improve interpretability while maintaining predictive performance.
This paper introduces a benchmark for semantic segmentation in low-resource dialectal Arabic and proposes a model that improves performance on conversational speech compared to standard baselines.
This paper introduces DiGSeg, a framework that repurposes pretrained diffusion models for state-of-the-art semantic and open-vocabulary segmentation by leveraging latent space conditioning and text-guided alignment.