no code implementations • 20 Jan 2024 • Jiahao Nie, Zhiwei He, Xudong Lv, Xueyi Zhou, Dong-Kyu Chae, Fei Xie
Based on this observation, we design a novel point set representation learning network inheriting transformer architecture, termed AdaFormer, which adaptively encodes the dynamically varying shape and size information from cross-category data in a unified manner.
1 code implementation • 16 Jan 2024 • Jiahao Nie, Yun Xing, Gongjie Zhang, Pei Yan, Aoran Xiao, Yap-Peng Tan, Alex C. Kot, Shijian Lu
Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars.
2 code implementations • NeurIPS 2023 • Yun Xing, Jian Kang, Aoran Xiao, Jiahao Nie, Ling Shao, Shijian Lu
Such semantic misalignment circulates in pre-training, leading to inferior zero-shot performance in dense predictions due to insufficient visual concepts captured in textual representations.
1 code implementation • 5 Sep 2023 • Yuxiang Yang, Yingqi Deng, Jing Zhang, Jiahao Nie, Zheng-Jun Zha
The spatial information indicating objects' spatial adjacency across consecutive frames is crucial for effective object tracking.
2 code implementations • 23 Apr 2023 • Jiahao Nie, Zhiwei He, Yuxiang Yang, Zhengyi Bao, Mingyu Gao, Jing Zhang
By integrating the derived classification scores with the center-ness scores, the resulting network can effectively suppress interference proposals and further mitigate task misalignment.
1 code implementation • 1 Apr 2023 • Jiahao Nie, Zhiwei He, Yuxiang Yang, Xudong Lv, Mingyu Gao, Jing Zhang
Incorporating this transformer-based voting scheme into 3D RPN, a novel Siamese method dubbed GLT-T is developed for 3D single object tracking on point clouds.
2 code implementations • 20 Nov 2022 • Jiahao Nie, Zhiwei He, Yuxiang Yang, Mingyu Gao, Jing Zhang
Technically, a global-local transformer (GLT) module is employed to integrate object- and patch-aware prior into seed point features to effectively form strong feature representation for geometric positions of the seed points, thus providing more robust and accurate cues for offset learning.
no code implementations • 29 Apr 2022 • Jiahao Nie, Han Wu, Zhiwei He, Yuxiang Yang, Mingyu Gao, Zhekang Dong
In this paper, to alleviate this misalignment, we propose a novel tracking paradigm, called SiamLA.