Zobrazeno 1 - 10
of 2 468
pro vyhledávání: '"Gao, Pan"'
Parameterized quantum circuits (PQCs) are crucial for quantum machine learning and circuit synthesis, enabling the practical implementation of complex quantum tasks. However, PQC learning has been largely confined to classical optimization methods, w
Externí odkaz:
http://arxiv.org/abs/2409.20044
Text-to-image diffusion models particularly Stable Diffusion, have revolutionized the field of computer vision. However, the synthesis quality often deteriorates when asked to generate images that faithfully represent complex prompts involving multip
Externí odkaz:
http://arxiv.org/abs/2409.19967
Recent progress of semantic point clouds analysis is largely driven by synthetic data (e.g., the ModelNet and the ShapeNet), which are typically complete, well-aligned and noisy free. Therefore, representations of those ideal synthetic point clouds h
Externí odkaz:
http://arxiv.org/abs/2409.06956
Stable diffusion networks have emerged as a groundbreaking development for their ability to produce realistic and detailed visual content. This characteristic renders them ideal decoders, capable of producing high-quality and aesthetically pleasing r
Externí odkaz:
http://arxiv.org/abs/2408.10543
Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation
Previous studies have demonstrated the effectiveness of point-based neural models on the point cloud analysis task. However, there remains a crucial issue on producing the efficient input embedding for raw point coordinates. Moreover, another issue l
Externí odkaz:
http://arxiv.org/abs/2407.08994
Image quality assessment (IQA) has long been a fundamental challenge in image understanding. In recent years, deep learning-based IQA methods have shown promising performance. However, the lack of large amounts of labeled data in the IQA field has hi
Externí odkaz:
http://arxiv.org/abs/2407.03886
Style transfer aims to render an image with the artistic features of a style image, while maintaining the original structure. Various methods have been put forward for this task, but some challenges still exist. For instance, it is difficult for CNN-
Externí odkaz:
http://arxiv.org/abs/2405.19775
Existing Blind image Super-Resolution (BSR) methods focus on estimating either kernel or degradation information, but have long overlooked the essential content details. In this paper, we propose a novel BSR approach, Content-aware Degradation-driven
Externí odkaz:
http://arxiv.org/abs/2405.07648
In the field of transportation, it is of paramount importance to address and mitigate illegal actions committed by both motor and non-motor vehicles. Among those actions, wrong-way cycling (i.e., riding a bicycle or e-bike in the opposite direction o
Externí odkaz:
http://arxiv.org/abs/2405.07293
Recently, unsupervised salient object detection (USOD) has gained increasing attention due to its annotation-free nature. However, current methods mainly focus on specific tasks such as RGB and RGB-D, neglecting the potential for task migration. In t
Externí odkaz:
http://arxiv.org/abs/2404.14759