AIDEN: Design and Pilot Study of an AI Assistant for the Visually Impaired

A new dataset named ClimateIQA has been introduced to enhance the capabilities of Vision-Language Models (VLMs) in analyzing meteorological anomalies. This dataset, which includes 26,280 high-quality images, aims to address the challenges faced by existing models like GPT-4o and Qwen-VL in interpreting complex meteorological heatmaps characterized by irregular shapes and color variations.

A new dataset, the Sesame Plant Segmentation Dataset, has been introduced, featuring 206 training images, 43 validation images, and 43 test images formatted for YOLO segmentation. This dataset focuses on sesame plants at early growth stages, captured under various environmental conditions in Nigeria, and annotated with the Segment Anything Model version 2.

A theoretical analysis of decentralization in autoregressive generation has been presented, introducing the Decentralized Discrete Flow Matching objective, which expresses probability generating velocity as a linear combination of expert flows. Experiments demonstrate the equivalence between decentralized and centralized training settings for multimodal language models, specifically comparing LLaVA and InternVL 2.5-1B.