Researchers present SpermYOLO, a coordinated YOLO-based detection model aimed at locating sperm cells in microscopic images for computer-assisted semen analysis. The work targets difficulties such as densely packed cells, visually similar artifacts, and sperm-like impurities that complicate automated detection. The paper is an arXiv preprint and reports on detection accuracy and efficiency.
A new arXiv paper applies hyperbolic geometry to open-world object detection in satellite and aerial imagery. The work targets the fact that remote-sensing object categories carry hidden hierarchical structure, which standard Euclidean embedding spaces struggle to represent. The method is designed to flag unknown objects and incrementally absorb them into the model once annotations become available.
A newly posted arXiv paper, cross-listed in computational linguistics and machine learning, presents Vague2Detect, a detection approach designed to work with vague or functional language prompts. The authors note that fixed-class detectors like YOLO and even open-vocabulary systems such as YOLO-World often misinterpret ambiguous wording and fail to match it to the intended objects. The proposed knowledge-based method aims to close this gap for real-world detection scenarios.
A new arXiv paper tackles few-shot object detection with vision-language models in domains that differ from their training data, such as aerial, industrial, and medical imagery, where only ten labeled images are available. Rather than optimizing discrete text prompts, the authors propose a soft prompting method for adaptation. The work is a cross-listing on arXiv cs.AI.
A revised arXiv paper presents a multi-modal perception pipeline aimed at object detection and tracking for autonomous racing vehicles. The work addresses robustness challenges such as degraded visibility, sensor noise, and sensor failures that remain difficult for driving perception systems. It falls under the cs.AI category and appears as a replacement submission.