Zobrazeno 1 - 10
of 449
pro vyhledávání: '"Jia Wenjing"'
The Vision Transformer (ViT) has achieved notable success in computer vision, with its variants extensively validated across various downstream tasks, including semantic segmentation. However, designed as general-purpose visual encoders, ViT backbone
Externí odkaz:
http://arxiv.org/abs/2411.17061
Images captured in challenging environments--such as nighttime, foggy, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality. Effective restoration of these degraded images is crit
Externí odkaz:
http://arxiv.org/abs/2409.18932
Autor:
Xu, Guoan, Huang, Wenfeng, Wu, Tao, Chen, Ligeng, Jia, Wenjing, Gao, Guangwei, Zhu, Xiatian, Perry, Stuart
Semantic segmentation involves assigning a specific category to each pixel in an image. While Vision Transformer-based models have made significant progress, current semantic segmentation methods often struggle with precise predictions in localized a
Externí odkaz:
http://arxiv.org/abs/2408.05699
Both Convolutional Neural Networks (CNNs) and Transformers have shown great success in semantic segmentation tasks. Efforts have been made to integrate CNNs with Transformer models to capture both local and global context interactions. However, there
Externí odkaz:
http://arxiv.org/abs/2407.07441
Healthcare monitoring is crucial, especially for the daily care of elderly individuals living alone. It can detect dangerous occurrences, such as falls, and provide timely alerts to save lives. Non-invasive millimeter wave (mmWave) radar-based health
Externí odkaz:
http://arxiv.org/abs/2405.01882
Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i.e., 1) the accumulation of false text segment detections, which aff
Externí odkaz:
http://arxiv.org/abs/2404.17151
Autor:
Xu, Chengpei, Fu, Hao, Ma, Long, Jia, Wenjing, Zhang, Chengqi, Xia, Feng, Ai, Xiaoyu, Li, Binghao, Zhang, Wenjie
Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancement (LLE) as the initial step followed by detector, LLE is primarily d
Externí odkaz:
http://arxiv.org/abs/2404.08965
In contrast to the abundant research focusing on large-scale models, the progress in lightweight semantic segmentation appears to be advancing at a comparatively slower pace. However, existing compact methods often suffer from limited feature represe
Externí odkaz:
http://arxiv.org/abs/2309.04914
Self-supervised representation learning (SSRL) has gained increasing attention in point cloud understanding, in addressing the challenges posed by 3D data scarcity and high annotation costs. This paper presents PCExpert, a novel SSRL approach that re
Externí odkaz:
http://arxiv.org/abs/2307.15569
Autor:
Huang, Ye, Kang, Di, Chen, Liang, Jia, Wenjing, He, Xiangjian, Duan, Lixin, Zhe, Xuefei, Bao, Linchao
Semantic segmentation has recently achieved notable advances by exploiting "class-level" contextual information during learning. However, these approaches simply concatenate class-level information to pixel features to boost the pixel representation
Externí odkaz:
http://arxiv.org/abs/2301.04258