AutoBrep: Autoregressive B-Rep Generation with Unified Topology and Geometry

A new study has introduced AbscessHeNe, a dataset of 4,926 contrast-enhanced CT slices specifically focused on head and neck abscesses, which are critical for timely diagnosis and treatment. This dataset aims to enhance the development of semantic segmentation models that can accurately identify abscess boundaries and assess deep neck space involvement.

A new framework named MLLMsent has been proposed to enhance the sentiment reasoning capabilities of Multimodal Large Language Models (MLLMs). This framework explores sentiment classification directly from images, sentiment analysis on generated image descriptions, and fine-tuning LLMs on sentiment-labeled descriptions, achieving state-of-the-art results in recent benchmarks.

The recent introduction of Mixture-of-Head attention (MoH) enhances the multi-head attention mechanism central to Transformer models, aiming to improve efficiency while maintaining or exceeding previous accuracy levels. This new architecture allows tokens to select relevant attention heads, thereby optimizing inference without increasing parameters.

A new framework named CORE has been proposed to enhance trajectory representation learning (TRL) by integrating context-aware route choice semantics into trajectory embeddings. This approach addresses the limitations of existing TRL methods that treat trajectories as static sequences, thereby enriching the semantic representation of urban mobility patterns.