3 accepted papers
In autonomous driving and robotic navigation, multi-sensor fusion technology has become increasingly mainstream, with precise sensor calibration as its foundation. Traditional calibration methods rely on manual effort or specific targets, limiting adaptability to complex environments. Learning-based
Anomaly Detection (AD) is an important and challenging task in computer vision. Recently, Contrastive Language-Image Pre-training (CLIP) has shown impressive generalization capability in zero-shot AD tasks. However, the inherent global semantic consistency of CLIP fails to accurately identify subtle…
Quantization is an effective way to compress neural networks. By reducing the bit width of the parameters, the processing efficiency of neural network models at edge devices can be notably improved. Most conventional quantization methods utilize real datasets to optimize quantization parameters and…