Zobrazeno 1 - 10
of 268
pro vyhledávání: '"Chen, Jianqi"'
Autor:
Hu, Panwen, Jiang, Jin, Chen, Jianqi, Han, Mingfei, Liao, Shengcai, Chang, Xiaojun, Liang, Xiaodan
The advent of AI-Generated Content (AIGC) has spurred research into automated video generation to streamline conventional processes. However, automating storytelling video production, particularly for customized narratives, remains challenging due to
Externí odkaz:
http://arxiv.org/abs/2411.04925
Autor:
Chen, Jianqi, Hu, Panwen, Chang, Xiaojun, Shi, Zhenwei, Kampffmeyer, Michael Christian, Liang, Xiaodan
Recent advancements in human motion synthesis have focused on specific types of motions, such as human-scene interaction, locomotion or human-human interaction, however, there is a lack of a unified system capable of generating a diverse combination
Externí odkaz:
http://arxiv.org/abs/2410.10790
Multimodal learning significantly benefits cancer survival prediction, especially the integration of pathological images and genomic data. Despite advantages of multimodal learning for cancer survival prediction, massive redundancy in multimodal data
Externí odkaz:
http://arxiv.org/abs/2401.01646
This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically
Externí odkaz:
http://arxiv.org/abs/2312.00956
Proportional-Integral-Derivative (PID) control has been the workhorse of control technology for about a century. Yet to this day, designing and tuning PID controllers relies mostly on either tabulated rules (Ziegler-Nichols) or on classical graphical
Externí odkaz:
http://arxiv.org/abs/2311.11460
Errors dynamics captures the evolution of the state errors between two distinct trajectories, that are governed by the same system rule but initiated or perturbed differently. In particular, state observer error dynamics analysis in matrix Lie group
Externí odkaz:
http://arxiv.org/abs/2307.16597
We propose a zero-shot approach to image harmonization, aiming to overcome the reliance on large amounts of synthetic composite images in existing methods. These methods, while showing promising results, involve significant training expenses and ofte
Externí odkaz:
http://arxiv.org/abs/2307.08182
Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achievin
Externí odkaz:
http://arxiv.org/abs/2307.04136
Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space. Despite some achievements in transferability and attack success rate, the crafted adversarial examples are easily perceived by human eyes. Towards visual impercep
Externí odkaz:
http://arxiv.org/abs/2305.08192
High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on
Externí odkaz:
http://arxiv.org/abs/2303.01681