Doonari OKuno's picture

Doonari OKuno

dooOkuno

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reacted to dronefreak's post with 🔥 about 8 hours ago

Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face. The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics. If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time. Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo Feedback, issues, and contributions are welcome.

liked a model about 8 hours ago

MiniMaxAI/MiniMax-M3

reacted to Jaward's post with 🔥 2 days ago

Our preprint is out! We attempt to model human teaching behaviors into agents yielding a unified framework that enables adaptive personalized learning experiences: LectūraAgents addresses the prevailing limitations in current AI learning systems with three essential capabilities: (1) a hierarchical multi-agent architecture modeled on academic standards. we observe that agents collaborating across hierarchies yield better personalized learning outcomes. (2) an adaptive embodied teaching mechanism, in which the instructor agent executes visible and pedagogically motivated teaching actions (e.g. handwrite, highlight, circle etc) on contents in a teaching environment while speaking. (3) to achieve this we propose a novel teaching action-speech alignment algorithm (TASA) that dynamically aligns speech with visual teaching actions: specifically, TASA temporally chops up speech segments into word-level tokens, performs salience heuristics analysis on learning contents (texts, images etc) then identifies relevant regions to apply pedagogical teaching actions that guide attention and augment understanding. We conducted several experiments to assess these capabilities: starting with pedagogical evaluation of the various components under frontier models, comparative analysis with existing frameworks and an efficacy study with real students. Results show consistent gains in standard instructional metrics (curated by expert educators) spanning lecture content quality, embodied teaching quality, assessment, and personalization over baseline systems, positioning LectūraAgents as a pedagogically grounded framework for personalized learning at scale. Paper: https://huggingface.co/papers/2606.16428 Data: https://huggingface.co/datasets/Jaward/lectura-agents-data

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