Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Xu, Fanjiang"'
Scene recognition, particularly for aerial and underwater images, often suffers from various types of degradation, such as blurring or overexposure. Previous works that focus on convolutional neural networks have been shown to be able to extract pano
Externí odkaz:
http://arxiv.org/abs/2409.14741
Due to the selective absorption and scattering of light by diverse aquatic media, underwater images usually suffer from various visual degradations. Existing underwater image enhancement (UIE) approaches that combine underwater physical imaging model
Externí odkaz:
http://arxiv.org/abs/2407.04230
Leveraging the development of structural causal model (SCM), researchers can establish graphical models for exploring the causal mechanisms behind machine learning techniques. As the complexity of machine learning applications rises, single-world int
Externí odkaz:
http://arxiv.org/abs/2406.11501
Autor:
Zhang, Jianqi, Wang, Jingyao, Sun, Chuxiong, Shen, Xingchen, Xu, Fanjiang, Zheng, Changwen, Qiang, Wenwen
Transformer-based methods are a mainstream approach for solving time series forecasting (TSF). These methods use temporal or variable tokens from observable data to make predictions. However, most focus on optimizing the model structure, with few stu
Externí odkaz:
http://arxiv.org/abs/2404.10337
Neural radiance field has achieved fundamental success in novel view synthesis from input views with the same brightness level captured under fixed normal lighting. Unfortunately, synthesizing novel views remains to be a challenge for input views wit
Externí odkaz:
http://arxiv.org/abs/2403.13337
Underwater video enhancement (UVE) aims to improve the visibility and frame quality of underwater videos, which has significant implications for marine research and exploration. However, existing methods primarily focus on developing image enhancemen
Externí odkaz:
http://arxiv.org/abs/2403.11506
Due to the advantages of fusing information from various modalities, multimodal learning is gaining increasing attention. Being a fundamental task of multimodal learning, Visual Grounding (VG), aims to locate objects in images through natural languag
Externí odkaz:
http://arxiv.org/abs/2403.01118
Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the structural rationale from graphs, thereby increasing the discriminability
Externí odkaz:
http://arxiv.org/abs/2312.10401
Underwater image enhancement (UIE) aims to generate clear images from low-quality underwater images. Due to the unavailability of clear reference images, researchers often synthesize them to construct paired datasets for training deep models. However
Externí odkaz:
http://arxiv.org/abs/2312.06240
Autor:
Si, Lingyu, Dong, Hongwei, Qiang, Wenwen, Yu, Junzhi, Zhai, Wenlong, Zheng, Changwen, Xu, Fanjiang, Sun, Fuchun
Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark knowledge has been a common way to solve such hard tasks. However, res
Externí odkaz:
http://arxiv.org/abs/2306.15977