Abstract: Modern diffusion-based image generative models have made significant progress and become promising to enrich training data for the object detection task. However, the generation quality and ...
Abstract: A Convolutional Neural Network (CNN) are a class of artificial neural networks specifically designed to process data with a grid-like topology, such as images, making them well-suited for ...
Abstract: Change detection (CD) is a hot research topic in the field of remote sensing (RS), and using convolutional neural networks (CNNs) and Transformers for CD tasks is the mainstream option ...
Abstract: In autonomous driving, understanding the surroundings is crucial for safety. Since most object detection systems are designed to identify known objects, they may miss unknown or novel ...
Abstract: Object detection in unmanned aerial vehicle (UAV) remote sensing imagery is significantly challenged by small-scale objects, dense distributions, and complex backgrounds. Existing detection ...
Abstract: Tiny-object detection is increasingly crucial in fields such as remote sensing, traffic monitoring, and robotics. Inspired by human visual perception, the attention mechanism has become a ...
Tian, Z., Shen, C., Chen, H., & He, T. (2019). FCOS: Fully convolutional one-stage object detection. In Proceedings of the IEEE/CVF conference on computer vision and ...
Abstract: Large-scale high-resolution remote sensing images (LSHR) are increasingly adopted for object detection, since they capture finer details. However, LSHR imposes a substantial computational ...
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