Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Hou, Xianxu"'
Large Language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications. However, training LLMs for languages other than English poses significant challenges, due to the
Externí odkaz:
http://arxiv.org/abs/2405.10626
Autor:
Gong, Haoyan, Feng, Yuzheng, Zhang, Zhenrong, Hou, Xianxu, Liu, Jingxin, Huang, Siqi, Liu, Hongbin
Vehicle license plate recognition is a crucial task in intelligent traffic management systems. However, the challenge of achieving accurate recognition persists due to motion blur from fast-moving vehicles. Despite the widespread use of image synthes
Externí odkaz:
http://arxiv.org/abs/2404.13677
Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations; (2) loss of texture and color information caused by noise
Externí odkaz:
http://arxiv.org/abs/2404.05253
Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the
Externí odkaz:
http://arxiv.org/abs/2212.11677
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions. In thi
Externí odkaz:
http://arxiv.org/abs/2203.13505
It has been widely known that CAM (Class Activation Map) usually only activates discriminative object regions and falsely includes lots of object-related backgrounds. As only a fixed set of image-level object labels are available to the WSSS (weakly
Externí odkaz:
http://arxiv.org/abs/2203.02668
Employing the latent space of pretrained generators has recently been shown to be an effective means for GAN-based face manipulation. The success of this approach heavily relies on the innate disentanglement of the latent space axes of the generator.
Externí odkaz:
http://arxiv.org/abs/2202.02713
Generative Adversarial Networks (GANs) have been widely adopted in various fields. However, existing GANs generally are not able to preserve the manifold of data space, mainly due to the simple representation of discriminator for the real/generated d
Externí odkaz:
http://arxiv.org/abs/2109.08955
There are large amount of valuable video archives in Video Home System (VHS) format. However, due to the analog nature, their quality is often poor. Compared to High-definition television (HDTV), VHS video not only has a dull color appearance but als
Externí odkaz:
http://arxiv.org/abs/2101.02384
Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there still lacks of control over the generation process in order to achieve semantic
Externí odkaz:
http://arxiv.org/abs/2012.11856