Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Ding, Henghui"'
Autor:
Ding, Henghui, Hong, Lingyi, Liu, Chang, Xu, Ning, Yang, Linjie, Fan, Yuchen, Miao, Deshui, Gu, Yameng, Li, Xin, He, Zhenyu, Wang, Yaowei, Yang, Ming-Hsuan, Chai, Jinming, Ma, Qin, Zhang, Junpei, Jiao, Licheng, Liu, Fang, Liu, Xinyu, Zhang, Jing, Zhang, Kexin, Liu, Xu, Li, LingLing, Fang, Hao, Pan, Feiyu, Lu, Xiankai, Zhang, Wei, Cong, Runmin, Tran, Tuyen, Cao, Bin, Zhang, Yisi, Wang, Hanyi, He, Xingjian, Liu, Jing
Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes. In this paper, we introduce the 6th Large-scale Video Object Segmentation (LSVOS) challenge in conjunction
Externí odkaz:
http://arxiv.org/abs/2409.05847
Autor:
Wu, Changli, Liu, Yihang, Ji, Jiayi, Ma, Yiwei, Wang, Haowei, Luo, Gen, Ding, Henghui, Sun, Xiaoshuai, Ji, Rongrong
3D Referring Expression Segmentation (3D-RES) is dedicated to segmenting a specific instance within a 3D space based on a natural language description. However, current approaches are limited to segmenting a single target, restricting the versatility
Externí odkaz:
http://arxiv.org/abs/2407.20664
Autor:
He, Shuting, Ding, Henghui
3D referring segmentation is an emerging and challenging vision-language task that aims to segment the object described by a natural language expression in a point cloud scene. The key challenge behind this task is vision-language feature fusion and
Externí odkaz:
http://arxiv.org/abs/2407.18244
Despite significant progress in 3D point cloud segmentation, existing methods primarily address specific tasks and depend on explicit instructions to identify targets, lacking the capability to infer and understand implicit user intentions in a unifi
Externí odkaz:
http://arxiv.org/abs/2407.13761
Autor:
Feng, Qian, Zhao, Hanbin, Zhang, Chao, Dong, Jiahua, Ding, Henghui, Jiang, Yu-Gang, Qian, Hui
Incremental Learning (IL) aims to learn deep models on sequential tasks continually, where each new task includes a batch of new classes and deep models have no access to task-ID information at the inference time. Recent vast pre-trained models (PTMs
Externí odkaz:
http://arxiv.org/abs/2407.03813
Autor:
Ding, Henghui, Liu, Chang, Wei, Yunchao, Ravi, Nikhila, He, Shuting, Bai, Song, Torr, Philip, Miao, Deshui, Li, Xin, He, Zhenyu, Wang, Yaowei, Yang, Ming-Hsuan, Xu, Zhensong, Yao, Jiangtao, Wu, Chengjing, Liu, Ting, Liu, Luoqi, Liu, Xinyu, Zhang, Jing, Zhang, Kexin, Yang, Yuting, Jiao, Licheng, Yang, Shuyuan, Gao, Mingqi, Luo, Jingnan, Yang, Jinyu, Han, Jungong, Zheng, Feng, Cao, Bin, Zhang, Yisi, Lin, Xuanxu, He, Xingjian, Zhao, Bo, Liu, Jing, Pan, Feiyu, Fang, Hao, Lu, Xiankai
Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentation Track based on MOSE dataset and Motion Expression guided Video Seg
Externí odkaz:
http://arxiv.org/abs/2406.17005
Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. It is widely studied in recent years as a promising and challenging field of Artificial Intelligence Generative Content (AIGC). Recent signific
Externí odkaz:
http://arxiv.org/abs/2406.14555
Semantic segmentation and semantic image synthesis are two representative tasks in visual perception and generation. While existing methods consider them as two distinct tasks, we propose a unified diffusion-based framework (SemFlow) and model them a
Externí odkaz:
http://arxiv.org/abs/2405.20282
In practical applications of human pose estimation, low-resolution inputs frequently occur, and existing state-of-the-art models perform poorly with low-resolution images. This work focuses on boosting the performance of low-resolution models by dist
Externí odkaz:
http://arxiv.org/abs/2405.11448
Randomized Smoothing (RS) has been proven a promising method for endowing an arbitrary image classifier with certified robustness. However, the substantial uncertainty inherent in the high-dimensional isotropic Gaussian noise imposes the curse of dim
Externí odkaz:
http://arxiv.org/abs/2404.09586