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pro vyhledávání: '"LIAO, Liang"'
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
This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effecti
Externí odkaz:
http://arxiv.org/abs/2412.06075
Online Domain Adaptation (OnDA) is designed to handle unforeseeable domain changes at minimal cost that occur during the deployment of the model, lacking clear boundaries between the domain, such as sudden weather events. However, existing OnDA metho
Externí odkaz:
http://arxiv.org/abs/2409.01072
Autor:
Chen, Chaofeng, Yang, Sensen, Wu, Haoning, Liao, Liang, Zhang, Zicheng, Wang, Annan, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Recent advances of large multi-modality models (LMM) have greatly improved the ability of image quality assessment (IQA) method to evaluate and explain the quality of visual content. However, these advancements are mostly focused on overall quality a
Externí odkaz:
http://arxiv.org/abs/2407.17035
Head-mounted 360{\deg} displays and portable 360{\deg} cameras have significantly progressed, providing viewers a realistic and immersive experience. However, many omnidirectional videos have low frame rates that can lead to visual fatigue, and the p
Externí odkaz:
http://arxiv.org/abs/2407.14066
Autor:
Wu, Haoning, Zhu, Hanwei, Zhang, Zicheng, Zhang, Erli, Chen, Chaofeng, Liao, Liang, Li, Chunyi, Wang, Annan, Sun, Wenxiu, Yan, Qiong, Liu, Xiaohong, Zhai, Guangtao, Wang, Shiqi, Lin, Weisi
Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more
Externí odkaz:
http://arxiv.org/abs/2402.16641
Autor:
Wu, Haoning, Zhang, Zicheng, Zhang, Weixia, Chen, Chaofeng, Liao, Liang, Li, Chunyi, Gao, Yixuan, Wang, Annan, Zhang, Erli, Sun, Wenxiu, Yan, Qiong, Min, Xiongkuo, Zhai, Guangtao, Lin, Weisi
The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents. While recent studies have demonstrated the exceptional potentials of la
Externí odkaz:
http://arxiv.org/abs/2312.17090
Autor:
Chen, Chaofeng, Zhou, Shangchen, Liao, Liang, Wu, Haoning, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. Existing methods such as Generative Adversarial Networks (GANs) or continuous diffusion models present th
Externí odkaz:
http://arxiv.org/abs/2312.05616
Text-to-image diffusion models are typically trained to optimize the log-likelihood objective, which presents challenges in meeting specific requirements for downstream tasks, such as image aesthetics and image-text alignment. Recent research address
Externí odkaz:
http://arxiv.org/abs/2311.15657
Autor:
Wu, Haoning, Zhang, Zicheng, Zhang, Erli, Chen, Chaofeng, Liao, Liang, Wang, Annan, Xu, Kaixin, Li, Chunyi, Hou, Jingwen, Zhai, Guangtao, Xue, Geng, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model. While existing foundation mod
Externí odkaz:
http://arxiv.org/abs/2311.06783