no code implementations • 8 May 2024 • Yaqi Wu, Zhihao Fan, Xiaofeng Chu, Jimmy S. Ren, Xiaoming Li, Zongsheng Yue, Chongyi Li, Shangcheng Zhou, Ruicheng Feng, Yuekun Dai, Peiqing Yang, Chen Change Loy, Senyan Xu, Zhijing Sun, Jiaying Zhu, Yurui Zhu, Xueyang Fu, Zheng-Jun Zha, Jun Cao, Cheng Li, Shu Chen, Liang Ma, Shiyang Zhou, Haijin Zeng, Kai Feng, Yongyong Chen, Jingyong Su, Xianyu Guan, Hongyuan Yu, Cheng Wan, Jiamin Lin, Binnan Han, Yajun Zou, Zhuoyuan Wu, Yuan Huang, Yongsheng Yu, Daoan Zhang, Jizhe Li, Xuanwu Yin, Kunlong Zuo, Yunfan Lu, Yijie Xu, Wenzong Ma, Weiyu Guo, Hui Xiong, Wei Yu, Bingchun Luo, Sabari Nathan, Priya Kansal
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
no code implementations • 27 Jun 2023 • Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chengyao Wang, Shu Liu, Jingyong Su, Jiaya Jia
We hope our work can benefit broader industrial applications where novel classes with limited annotations are required to be decently identified.
1 code implementation • ICCV 2023 • Zhuofan Zong, Dongzhi Jiang, Guanglu Song, Zeyue Xue, Jingyong Su, Hongsheng Li, Yu Liu
The HoP approach is straightforward: given the current timestamp t, we generate a pseudo Bird's-Eye View (BEV) feature of timestamp t-k from its adjacent frames and utilize this feature to predict the object set at timestamp t-k. Our approach is motivated by the observation that enforcing the detector to capture both the spatial location and temporal motion of objects occurring at historical timestamps can lead to more accurate BEV feature learning.
Ranked #3 on 3D Object Detection on nuScenes Camera Only
1 code implementation • CVPR 2023 • Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chenyao Wang, Shu Liu, Jingyong Su, Jiaya Jia
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations.
Ranked #7 on Few-Shot Semantic Segmentation on COCO-20i (1-shot)
1 code implementation • 12 Aug 2022 • Zhengcen Li, Yueran Li, Linlin Tang, Tong Zhang, Jingyong Su
To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints.
1 code implementation • 7 Mar 2016 • Rushil Anirudh, Pavan Turaga, Jingyong Su, Anuj Srivastava
We propose to learn an embedding such that each action trajectory is mapped to a single point in a low-dimensional Euclidean space, and the trajectories that differ only in temporal rates map to the same point.
no code implementations • CVPR 2015 • Fillipe Souza, Sudeep Sarkar, Anuj Srivastava, Jingyong Su
Graph-theoretical methods have successfully provided semantic and structural interpretations of images and videos.
no code implementations • CVPR 2015 • Rushil Anirudh, Pavan Turaga, Jingyong Su, Anuj Srivastava
Learning an accurate low dimensional embedding for actions could have a huge impact in the areas of efficient search and retrieval, visualization, learning, and recognition.
no code implementations • 23 Mar 2015 • Zhengwu Zhang, Jingyong Su, Eric Klassen, Huiling Le, Anuj Srivastava
Using a natural Riemannain metric on vector bundles of SPDMs, we compute geodesic paths and geodesic distances between trajectories in the quotient space of this vector bundle, with respect to the re-parameterization group.
no code implementations • CVPR 2014 • Jingyong Su, Anuj Srivastava, Fillipe D. M. de Souza, Sudeep Sarkar
We apply this framework to the problem of speech recognition using both audio and visual components.