Zobrazeno 1 - 10
of 12 332
pro vyhledávání: '"Nong P"'
Timely and effective vulnerability patching is essential for cybersecurity defense, for which various approaches have been proposed yet still struggle to generate valid and correct patches for real-world vulnerabilities. In this paper, we leverage th
Externí odkaz:
http://arxiv.org/abs/2408.13597
Detecting vulnerabilities is a crucial task for maintaining the integrity, availability, and security of software systems. Utilizing DL-based models for vulnerability detection has become commonplace in recent years. However, such deep learning-based
Externí odkaz:
http://arxiv.org/abs/2408.04125
Automated Machine Learning (AutoML) offers a promising approach to streamline the training of machine learning models. However, existing AutoML frameworks are often limited to unimodal scenarios and require extensive manual configuration. Recent adva
Externí odkaz:
http://arxiv.org/abs/2408.00665
Autor:
Nong, X. D., Liang, N.
In this paper, we utilize recent observational data from Gamma-Ray Bursts (GRBs) and Pantheon+ Supernovae Ia (SNe Ia) sample to explore the interacting Dark Energy (IDE) model in a phenomenological scenario. Results from GRBs-only, SNe Ia and GRBs+SN
Externí odkaz:
http://arxiv.org/abs/2407.16644
Currently, the integration of mobile Graphical User Interfaces (GUIs) is ubiquitous in most people's daily lives. And the ongoing evolution of multimodal large-scale models, such as GPT-4v, Qwen-VL-Max, has significantly bolstered the capabilities of
Externí odkaz:
http://arxiv.org/abs/2407.04346
Publikováno v:
44th Asian Conference on Remote Sensing, ACRS 2023. Code 198676
In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification
Externí odkaz:
http://arxiv.org/abs/2406.14220
Autor:
Zhang, Huaxin, Xu, Xiaohao, Wang, Xiang, Zuo, Jialong, Han, Chuchu, Huang, Xiaonan, Gao, Changxin, Wang, Yuehuan, Sang, Nong
Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address these drawbacks, we propose Holmes-VAD, a novel framework that levera
Externí odkaz:
http://arxiv.org/abs/2406.12235
Open-vocabulary semantic segmentation is a challenging task, which requires the model to output semantic masks of an image beyond a close-set vocabulary. Although many efforts have been made to utilize powerful CLIP models to accomplish this task, th
Externí odkaz:
http://arxiv.org/abs/2406.09829
Autor:
Wang, Xiang, Zhang, Shiwei, Gao, Changxin, Wang, Jiayu, Zhou, Xiaoqiang, Zhang, Yingya, Yan, Luxin, Sang, Nong
Recent diffusion-based human image animation techniques have demonstrated impressive success in synthesizing videos that faithfully follow a given reference identity and a sequence of desired movement poses. Despite this, there are still two limitati
Externí odkaz:
http://arxiv.org/abs/2406.01188
Autor:
Xu, Zhengze, Chen, Mengting, Wang, Zhao, Xing, Linyu, Zhai, Zhonghua, Sang, Nong, Lan, Jinsong, Xiao, Shuai, Gao, Changxin
Video try-on is a challenging task and has not been well tackled in previous works. The main obstacle lies in preserving the details of the clothing and modeling the coherent motions simultaneously. Faced with those difficulties, we address video try
Externí odkaz:
http://arxiv.org/abs/2404.17571