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of 276
pro vyhledávání: '"Qi, Honggang"'
In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to represent driving-specific scenarios accurately, particularly in complex interactions and lo
Externí odkaz:
http://arxiv.org/abs/2411.13076
DeepFake technology has gained significant attention due to its ability to manipulate facial attributes with high realism, raising serious societal concerns. Face-Swap DeepFake is the most harmful among these techniques, which fabricates behaviors by
Externí odkaz:
http://arxiv.org/abs/2409.03200
Autor:
Liu, Zheng, Qi, Honggang
Video post-processing methods can improve the quality of compressed videos at the decoder side. Most of the existing methods need to train corresponding models for compressed videos with different quantization parameters to improve the quality of com
Externí odkaz:
http://arxiv.org/abs/2311.08746
DeepFakes have raised serious societal concerns, leading to a great surge in detection-based forensics methods in recent years. Face forgery recognition is a standard detection method that usually follows a two-phase pipeline. While those methods per
Externí odkaz:
http://arxiv.org/abs/2308.01520
Face-swap DeepFake is an emerging AI-based face forgery technique that can replace the original face in a video with a generated face of the target identity while retaining consistent facial attributes such as expression and orientation. Due to the h
Externí odkaz:
http://arxiv.org/abs/2307.14593
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks
Convolutional neural networks are prevailing in deep learning tasks. However, they suffer from massive cost issues when working on mobile devices. Network pruning is an effective method of model compression to handle such problems. This paper present
Externí odkaz:
http://arxiv.org/abs/2205.03602
There are two mainstreams for object detection: top-down and bottom-up. The state-of-the-art approaches mostly belong to the first category. In this paper, we demonstrate that the bottom-up approaches are as competitive as the top-down and enjoy high
Externí odkaz:
http://arxiv.org/abs/2204.08394
Publikováno v:
In Phytomedicine December 2024 135
Autor:
Li, Zhiqiang, Jiang, Jie, Chen, Xi, Zhang, Min, Wang, Yong, Li, Qingli, Qi, Honggang, Liu, Min, Laganière, Robert
Publikováno v:
In Expert Systems With Applications 1 May 2024 241
Autor:
Wu, Junhao, Chen, Xi, Li, Rui, Wang, Anqi, Huang, Shutong, Li, Qingli, Qi, Honggang, Liu, Min, Cheng, Heqin, Wang, Zhaocai
Publikováno v:
In Journal of Environmental Management April 2024 357