Multiple full Vision Transformer (ViT) object detection models exist, including LW-DETR, RF-DETR, YOLOS, ViTDet, and WB-DETR, each differing in architectural purity and usability. The choice depends o…
Svetovid, a new WebGPU-powered tool, enables real-time visualization and debugging of PyTorch models, NumPy arrays, and large datasets directly in the browser. The tool supports interactive exploratio…
Object detection models have evolved from two-stage detectors like R-CNN to one-stage models such as YOLO and transformer-based architectures like DETR, each offering trade-offs between accuracy and s…
Researchers introduced SPANUQ, a lightweight probe for span-level uncertainty quantification in large language models, achieving 10-20x faster inference than sampling-based methods while outperforming…
Researchers introduced RT-SFOD, a new object detection method that improves speed and accuracy by building on YOLOv10 and using dual-head pseudo-label fusion and multi-scale feature diversification. T…
A new dissertation proposes three novel architectures to improve machine intelligence across vision-language tasks, including image captioning, visual dialog, and interactive instruction following. Th…
A team of researchers improved a DETR-based fusion transformer for the MaCVi 2026 Vision-to-Chart data association challenge by adding a dedicated MLP, QueryMLP, to explicitly predict a buoy's waterli…