Zobrazeno 1 - 10
of 629
pro vyhledávání: '"ZHU Jiawen"'
Publikováno v:
E3S Web of Conferences, Vol 565, p 01013 (2024)
Rapid urbanisation in Ya’an City, Sichuan Province, has caused environmental pollution and energy depletion in all districts, and the situation of coupled coordinated development is critical. In this study, with the help of PSR model, indicators ar
Externí odkaz:
https://doaj.org/article/615ac429a18a425ba55825c0fb484603
Autor:
Jiang, Haoyu, Cheng, Zhi-Qi, Moreira, Gabriel, Zhu, Jiawen, Sun, Jingdong, Ren, Bukun, He, Jun-Yan, Dai, Qi, Hua, Xian-Sheng
Universal Cross-Domain Retrieval (UCDR) retrieves relevant images from unseen domains and classes without semantic labels, ensuring robust generalization. Existing methods commonly employ prompt tuning with pre-trained vision-language models but are
Externí odkaz:
http://arxiv.org/abs/2412.10680
Cross-modal metric learning is a prominent research topic that bridges the semantic heterogeneity between vision and language. Existing methods frequently utilize simple cosine or complex distance metrics to transform the pairwise features into a sim
Externí odkaz:
http://arxiv.org/abs/2410.15266
Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained vision-language models to detect anomalies in a target dataset without using any dataset-specific training or demonstration. However, these meth
Externí odkaz:
http://arxiv.org/abs/2410.10289
Existing RGB-T tracking algorithms have made remarkable progress by leveraging the global interaction capability and extensive pre-trained models of the Transformer architecture. Nonetheless, these methods mainly adopt imagepair appearance matching a
Externí odkaz:
http://arxiv.org/abs/2408.07889
Data exploration is an important aspect of the workflow of mixed-methods researchers, who conduct both qualitative and quantitative analysis. However, there currently exists few tools that adequately support both types of analysis simultaneously, for
Externí odkaz:
http://arxiv.org/abs/2405.19580
Mainstream approaches to spectral reconstruction (SR) primarily focus on designing Convolution- and Transformer-based architectures. However, CNN methods often face challenges in handling long-range dependencies, whereas Transformers are constrained
Externí odkaz:
http://arxiv.org/abs/2405.07777
Autor:
Zhu, Jiawen, Chen, Xin, Diao, Haiwen, Li, Shuai, He, Jun-Yan, Li, Chenyang, Luo, Bin, Wang, Dong, Lu, Huchuan
The speed-precision trade-off is a critical problem for visual object tracking which usually requires low latency and deployment on constrained resources. Existing solutions for efficient tracking mainly focus on adopting light-weight backbones or mo
Externí odkaz:
http://arxiv.org/abs/2403.17651
Autor:
Zhu, Jiawen, Pang, Guansong
This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the targe
Externí odkaz:
http://arxiv.org/abs/2403.06495
Autor:
An, Pengcheng, Zhu, Jiawen, Zhang, Zibo, Yin, Yifei, Ma, Qingyuan, Yan, Che, Du, Linghao, Zhao, Jian
Voice messages, by nature, prevent users from gauging the emotional tone without fully diving into the audio content. This hinders the shared emotional experience at the pre-retrieval stage. Research scarcely explored "Emotional Teasers"-pre-retrieva
Externí odkaz:
http://arxiv.org/abs/2402.07174