Zobrazeno 1 - 10
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pro vyhledávání: '"Ke, Wei"'
Autor:
Pan, Lingzhi, Zhang, Tong, Chen, Bingyuan, Zhou, Qi, Ke, Wei, Süsstrunk, Sabine, Salzmann, Mathieu
With the advancements in denoising diffusion probabilistic models (DDPMs), image inpainting has significantly evolved from merely filling information based on nearby regions to generating content conditioned on various prompts such as text, exemplar
Externí odkaz:
http://arxiv.org/abs/2407.08019
Multi-label image classification datasets are often partially labeled where many labels are missing, posing a significant challenge to training accurate deep classifiers. However, the powerful Mixup sample-mixing data augmentation cannot be well util
Externí odkaz:
http://arxiv.org/abs/2405.15860
Model-Driven Engineering (MDE) is a technique that aims to boost productivity in software development and ensure the safety of critical systems. Central to MDE is the refinement of high-level requirement models into executable code. Given that requir
Externí odkaz:
http://arxiv.org/abs/2405.10060
In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can
Externí odkaz:
http://arxiv.org/abs/2404.07504
The pre-trained vision-language model, exemplified by CLIP, advances zero-shot semantic segmentation by aligning visual features with class embeddings through a transformer decoder to generate semantic masks. Despite its effectiveness, prevailing met
Externí odkaz:
http://arxiv.org/abs/2403.08426
Autor:
Chong, Chak Fong, Fang, Xinyi, Guo, Jielong, Wang, Yapeng, Ke, Wei, Lam, Chan-Tong, Im, Sio-Kei
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image. Assigning pseudo-labels to unknown labels to gain additional training signals has become prevalent for training deep classification
Externí odkaz:
http://arxiv.org/abs/2401.16991
Autor:
Xiong, Xiangyu, Sun, Yue, Liu, Xiaohong, Ke, Wei, Lam, Chan-Tong, Chen, Jiangang, Jiang, Mingfeng, Wang, Mingwei, Xie, Hui, Tong, Tong, Gao, Qinquan, Chen, Hao, Tan, Tao
Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generat
Externí odkaz:
http://arxiv.org/abs/2312.17538
Autor:
Ke, Wei
We consider the uniqueness of the following positive solutions of $m$-Laplacian equation: \begin{equation} \left\{ \begin{aligned} -\Delta _m u&=\lambda u^{m-1}+u^{p-1} \quad \text{in} \quad \Omega\\ u&=0 \quad \text{on} \quad \partial \Omega \end{al
Externí odkaz:
http://arxiv.org/abs/2312.15007
Autor:
Lin, Zhongjin, Shastri, Bhavin J., Yu, Shangxuan, Song, Jingxiang, Zhu, Yuntao, Safarnejadian, Arman, Cai, Wangning, Lin, Yanmei, Ke, Wei, Hammood, Mustafa, Wang, Tianye, Xu, Mengyue, Zheng, Zibo, Al-Qadasi, Mohammed, Esmaeeli, Omid, Rahim, Mohamed, Pakulski, Grzegorz, Schmid, Jens, Barrios, Pedro, Jiang, Weihong, Morison, Hugh, Mitchell, Matthew, Qiang, Xiaogang, Guan, Xun, Jaeger, Nicolas A. F., Rusch, Leslie A. n, Shekhar, Sudip, Shi, Wei, Yu, Siyuan, Cai, Xinlun, Chrostowski, Lukas
Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by providing low latency, high bandwidth, and energy-efficient computations. Here, we introduce a photonic tensor core processor enabled by time-mul
Externí odkaz:
http://arxiv.org/abs/2311.16896
Autor:
Xiong, Xiangyu, Sun, Yue, Liu, Xiaohong, Lam, Chan-Tong, Tong, Tong, Chen, Hao, Gao, Qinquan, Ke, Wei, Tan, Tao
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularl
Externí odkaz:
http://arxiv.org/abs/2311.14388