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pro vyhledávání: '"PENG, Tao"'
Vehicle detection and tracking in satellite video is essential in remote sensing (RS) applications. However, upon the statistical analysis of existing datasets, we find that the dim vehicles with low radiation intensity and limited contrast against t
Externí odkaz:
http://arxiv.org/abs/2412.18214
Current saliency-based defect detection methods show promise in industrial settings, but the unpredictability of defects in steel production environments complicates dataset creation, hampering model performance. Existing data augmentation approaches
Externí odkaz:
http://arxiv.org/abs/2412.15570
Learning lighting adaption is a key step in obtaining a good visual perception and supporting downstream vision tasks. There are multiple light-related tasks (e.g., image retouching and exposure correction) and previous studies have mainly investigat
Externí odkaz:
http://arxiv.org/abs/2412.01493
Autor:
Yang, Qirui, Li, Yinbo, Jiang, Peng-Tao, Cheng, Qihua, Yu, Biting, Liu, Yihao, Yue, Huanjing, Yang, Jingyu
Previous tone mapping methods mainly focus on how to enhance tones in low-resolution images and recover details using the high-frequent components extracted from the input image. These methods typically rely on traditional feature pyramids to artific
Externí odkaz:
http://arxiv.org/abs/2412.01463
Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders th
Externí odkaz:
http://arxiv.org/abs/2410.21708
We present ClearSR, a new method that can better take advantage of latent low-resolution image (LR) embeddings for diffusion-based real-world image super-resolution (Real-ISR). Previous Real-ISR models mostly focus on how to activate more generative
Externí odkaz:
http://arxiv.org/abs/2410.14279
Real-world image super-resolution (Real-ISR) aims at restoring high-quality (HQ) images from low-quality (LQ) inputs corrupted by unknown and complex degradations. In particular, pretrained text-to-image (T2I) diffusion models provide strong generati
Externí odkaz:
http://arxiv.org/abs/2410.13807
In the realm of high-resolution (HR), fine-grained image segmentation, the primary challenge is balancing broad contextual awareness with the precision required for detailed object delineation, capturing intricate details and the finest edges of obje
Externí odkaz:
http://arxiv.org/abs/2410.10105
Autor:
Li, Ziyu, Gu, Tianyi, Wei, Wenqi, Yuan, Yang, Wang, Zhuo, Luo, Kangjian, Pan, Yupeng, Xie, Jianfeng, Zhang, Shaozhe, Peng, Tao, Liu, Lin, Chen, Qi, Han, Xiaotao, Luo, Yongkang, Li, Liang
Conductor materials with good mechanical performance as well as high electrical- and thermal-conductivities are particularly important to break through the current bottle-neck limit ($\sim 100$ T) of pulsed magnets. Here we perform systematic studies
Externí odkaz:
http://arxiv.org/abs/2410.09376
Autor:
Xia, Ruihao, Liang, Yu, Jiang, Peng-Tao, Zhang, Hao, Sun, Qianru, Tang, Yang, Li, Bo, Zhou, Pan
Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and
Externí odkaz:
http://arxiv.org/abs/2410.06593