Výsledky vyhledávání - "Zhang Dongyang"

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Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation

Autor: Li, Muquan, Zhang, Dongyang, He, Tao, Xie, Xiurui, Li, Yuan-Fang, Qin, Ke

Data-free knowledge distillation (DFKD) has emerged as a pivotal technique in the domain of model compression, substantially reducing the dependency on the original training data. Nonetheless, conventional DFKD methods that employ synthesized trainin

Externí odkaz: http://arxiv.org/abs/2410.17606

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Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

Autor: Conde, Marcos V., Lei, Zhijun, Li, Wen, Stejerean, Cosmin, Katsavounidis, Ioannis, Timofte, Radu, Yoon, Kihwan, Gankhuyag, Ganzorig, Lv, Jiangtao, Sun, Long, Pan, Jinshan, Dong, Jiangxin, Tang, Jinhui, Li, Zhiyuan, Wei, Hao, Ge, Chenyang, Zhang, Dongyang, Liu, Tianle, Chen, Huaian, Jin, Yi, Zhou, Menghan, Yan, Yiqiang, Gao, Si, Wu, Biao, Liu, Shaoli, Zheng, Chengjian, Zhang, Diankai, Wang, Ning, Qiu, Xintao, Zhou, Yuanbo, Wu, Kongxian, Dai, Xinwei, Tang, Hui, Deng, Wei, Gao, Qingquan, Tong, Tong, Lee, Jae-Hyeon, Choi, Ui-Jin, Yan, Min, Liu, Xin, Wang, Qian, Ye, Xiaoqian, Du, Zhan, Zhang, Tiansen, Peng, Long, Guo, Jiaming, Di, Xin, Liao, Bohao, Du, Zhibo, Xia, Peize, Pei, Renjing, Wang, Yang, Cao, Yang, Zha, Zhengjun, Han, Bingnan, Yu, Hongyuan, Wu, Zhuoyuan, Wan, Cheng, Liu, Yuqing, Yu, Haodong, Li, Jizhe, Huang, Zhijuan, Huang, Yuan, Zou, Yajun, Guan, Xianyu, Jia, Qi, Zhang, Heng, Yin, Xuanwu, Zuo, Kunlong, Moon, Hyeon-Cheol, Jeong, Tae-hyun, Yang, Yoonmo, Kim, Jae-Gon, Jeong, Jinwoo, Kim, Sunjei

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a dive

Externí odkaz: http://arxiv.org/abs/2404.16484

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Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning

Autor: He, Tao, Wu, Tongtong, Zhang, Dongyang, Duan, Guiduo, Qin, Ke, Li, Yuan-Fang

Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning process for SGG. This conventional paradigm may necessitate repetitive tra

Externí odkaz: http://arxiv.org/abs/2401.14626

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