Zobrazeno 1 - 10
of 332
pro vyhledávání: '"He, Shengfeng"'
Autor:
Yang, Jingru, Cao, Chengzhi, Xu, Chentianye, Xie, Zhongwei, Huang, Kaixiang, Zhou, Yang, He, Shengfeng
Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) face inherent challenges in image matting, particularly in preserving fine structural details. ViTs, with their global receptive field enabled by the self-attention mechanism, often
Externí odkaz:
http://arxiv.org/abs/2411.10251
Multimodal Large Language Models (MLLMs) excel at descriptive tasks within images but often struggle with precise object localization, a critical element for reliable visual interpretation. In contrast, traditional object detection models provide hig
Externí odkaz:
http://arxiv.org/abs/2411.10252
Long-term motion generation is a challenging task that requires producing coherent and realistic sequences over extended durations. Current methods primarily rely on framewise motion representations, which capture only static spatial details and over
Externí odkaz:
http://arxiv.org/abs/2409.01522
Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities, yet balancing reconstruction fidelity and editability for real images remains a significant challenge. In this work, we introduce \textbf{T}as
Externí odkaz:
http://arxiv.org/abs/2408.13395
Autor:
Yang, Haoxin, Xu, Xuemiao, Xu, Cheng, Zhang, Huaidong, Qin, Jing, Wang, Yi, Heng, Pheng-Ann, He, Shengfeng
Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as en
Externí odkaz:
http://arxiv.org/abs/2408.09458
Autor:
Jiang, Xinjie, Zheng, Chenxi, Xu, Xuemiao, Liu, Bangzhen, Zheng, Weiying, Zhang, Huaidong, He, Shengfeng
Video Visual Relation Detection (VidVRD) focuses on understanding how entities interact over time and space in videos, a key step for gaining deeper insights into video scenes beyond basic visual tasks. Traditional methods for VidVRD, challenged by i
Externí odkaz:
http://arxiv.org/abs/2408.09408
Drone-based crowd tracking faces difficulties in accurately identifying and monitoring objects from an aerial perspective, largely due to their small size and close proximity to each other, which complicates both localization and tracking. To address
Externí odkaz:
http://arxiv.org/abs/2407.17272
Autor:
Huang, Zikai, Xu, Xuemiao, Xu, Cheng, Zhang, Huaidong, Zheng, Chenxi, Qin, Jing, He, Shengfeng
Dance, as an art form, fundamentally hinges on the precise synchronization with musical beats. However, achieving aesthetically pleasing dance sequences from music is challenging, with existing methods often falling short in controllability and beat
Externí odkaz:
http://arxiv.org/abs/2407.07554
Autor:
Zhu, Huilin, Yuan, Jingling, Yang, Zhengwei, Guo, Yu, Wang, Zheng, Zhong, Xian, He, Shengfeng
Zero-shot object counting (ZOC) aims to enumerate objects in images using only the names of object classes during testing, without the need for manual annotations. However, a critical challenge in current ZOC methods lies in their inability to identi
Externí odkaz:
http://arxiv.org/abs/2407.04948
In real-world scenarios, image impairments often manifest as composite degradations, presenting a complex interplay of elements such as low light, haze, rain, and snow. Despite this reality, existing restoration methods typically target isolated degr
Externí odkaz:
http://arxiv.org/abs/2407.04621