Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Chen, Ying-cong"'
Multi-task dense scene understanding, which trains a model for multiple dense prediction tasks, has a wide range of application scenarios. Capturing long-range dependency and enhancing cross-task interactions are crucial to multi-task dense predictio
Externí odkaz:
http://arxiv.org/abs/2408.15101
Multi-task dense scene understanding, which learns a model for multiple dense prediction tasks, has a wide range of application scenarios. Modeling long-range dependency and enhancing cross-task interactions are crucial to multi-task dense prediction
Externí odkaz:
http://arxiv.org/abs/2407.02228
Visual grounding is an essential tool that links user-provided text queries with query-specific regions within an image. Despite advancements in visual grounding models, their ability to comprehend complex queries remains limited. To overcome this li
Externí odkaz:
http://arxiv.org/abs/2405.17104
Autor:
Tang, Jiaqi, Lu, Hao, Wu, Ruizheng, Xu, Xiaogang, Ma, Ke, Fang, Cheng, Guo, Bin, Lu, Jiangbo, Chen, Qifeng, Chen, Ying-Cong
Video Anomaly Detection (VAD) systems can autonomously monitor and identify disturbances, reducing the need for manual labor and associated costs. However, current VAD systems are often limited by their superficial semantic understanding of scenes an
Externí odkaz:
http://arxiv.org/abs/2405.16886
Autor:
Shen, Guibao, Wang, Luozhou, Lin, Jiantao, Ge, Wenhang, Zhang, Chaozhe, Tao, Xin, Zhang, Yuan, Wan, Pengfei, Wang, Zhongyuan, Chen, Guangyong, Li, Yijun, Chen, Ying-Cong
Recent advancements in text-to-image generation have been propelled by the development of diffusion models and multi-modality learning. However, since text is typically represented sequentially in these models, it often falls short in providing accur
Externí odkaz:
http://arxiv.org/abs/2405.15321
Autor:
Kong, Lingdong, Xie, Shaoyuan, Hu, Hanjiang, Niu, Yaru, Ooi, Wei Tsang, Cottereau, Benoit R., Ng, Lai Xing, Ma, Yuexin, Zhang, Wenwei, Pan, Liang, Chen, Kai, Liu, Ziwei, Qiu, Weichao, Zhang, Wei, Cao, Xu, Lu, Hao, Chen, Ying-Cong, Kang, Caixin, Zhou, Xinning, Ying, Chengyang, Shang, Wentao, Wei, Xingxing, Dong, Yinpeng, Yang, Bo, Jiang, Shengyin, Ma, Zeliang, Ji, Dengyi, Li, Haiwen, Huang, Xingliang, Tian, Yu, Kou, Genghua, Jia, Fan, Liu, Yingfei, Wang, Tiancai, Li, Ying, Hao, Xiaoshuai, Yang, Yifan, Zhang, Hui, Wei, Mengchuan, Zhou, Yi, Zhao, Haimei, Zhang, Jing, Li, Jinke, He, Xiao, Cheng, Xiaoqiang, Zhang, Bingyang, Zhao, Lirong, Ding, Dianlei, Liu, Fangsheng, Yan, Yixiang, Wang, Hongming, Ye, Nanfei, Luo, Lun, Tian, Yubo, Zuo, Yiwei, Cao, Zhe, Ren, Yi, Li, Yunfan, Liu, Wenjie, Wu, Xun, Mao, Yifan, Li, Ming, Liu, Jian, Liu, Jiayang, Qin, Zihan, Chu, Cunxi, Xu, Jialei, Zhao, Wenbo, Jiang, Junjun, Liu, Xianming, Wang, Ziyan, Li, Chiwei, Li, Shilong, Yuan, Chendong, Yang, Songyue, Liu, Wentao, Chen, Peng, Zhou, Bin, Wang, Yubo, Zhang, Chi, Sun, Jianhang, Chen, Hai, Yang, Xiao, Wang, Lizhong, Fu, Dongyi, Lin, Yongchun, Yang, Huitong, Li, Haoang, Luo, Yadan, Cheng, Xianjing, Xu, Yong
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impa
Externí odkaz:
http://arxiv.org/abs/2405.08816
In this paper, we study a new problem, Film Removal (FR), which attempts to remove the interference of wrinkled transparent films and reconstruct the original information under films for industrial recognition systems. We first physically model the i
Externí odkaz:
http://arxiv.org/abs/2403.04368
Autor:
Tang, Jiaqi, Lu, Hao, Xu, Xiaogang, Wu, Ruizheng, Hu, Sixing, Zhang, Tong, Cheng, Tsz Wa, Ge, Ming, Chen, Ying-Cong, Tsung, Fugee
Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing. Yet, many methods, tailored to specific pipelines, grapple with diverse product portfolios and evolving processes. Addressing this, we present the Increment
Externí odkaz:
http://arxiv.org/abs/2312.08917
Deep neural networks (DNNs) have been proven extremely susceptible to adversarial examples, which raises special safety-critical concerns for DNN-based autonomous driving stacks (i.e., 3D object detection). Although there are extensive works on image
Externí odkaz:
http://arxiv.org/abs/2309.01351
Autor:
Lin, Baijiong, Jiang, Weisen, Ye, Feiyang, Zhang, Yu, Chen, Pengguang, Chen, Ying-Cong, Liu, Shu, Kwok, James T.
Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields. However, task balancing problem remains a significant challenge in MTL, with the disparity in loss/gradient s
Externí odkaz:
http://arxiv.org/abs/2308.12029