no code implementations • 6 Jan 2025 • Yifeng Zhang, Bryan Baker, Shi Chen, Chao Zhang, Yu Huang, Qi Zhao, Sthitie Bom
The growing availability of sensors within semiconductor manufacturing processes makes it feasible to detect defective wafers with data-driven models.
no code implementations • 2 Sep 2024 • Leqi Shen, Tianxiang Hao, Sicheng Zhao, Yifeng Zhang, Pengzhang Liu, Yongjun Bao, Guiguang Ding
In this work, we argue that temporal redundancy significantly contributes to the model's high complexity due to the repeated information in consecutive frames.
2 code implementations • 2 Sep 2024 • Haohao Qu, Yifeng Zhang, Liangbo Ning, Wenqi Fan, Qing Li
Sequential recommendation methods are crucial in modern recommender systems for their remarkable capability to understand a user's changing interests based on past interactions.
no code implementations • 11 May 2024 • Qing Wu, Xu Guo, Lixuan Chen, Yanyan Liu, Dongming He, Xudong Wang, Xueli Chen, Yifeng Zhang, S. Kevin Zhou, Jingyi Yu, Yuyao Zhang
In this work, we propose Density neural representation (Diner), a novel unsupervised MAR method.
no code implementations • 20 Apr 2023 • Harsh Goel, Yifeng Zhang, Mehul Damani, Guillaume Sartoretti
To address these problems, we propose a new MARL method for traffic signal control, SocialLight, which learns cooperative traffic control policies by distributedly estimating the individual marginal contribution of agents on their local neighborhood.
1 code implementation • ICCV 2023 • Yifeng Zhang, Shi Chen, Qi Zhao
Answering visual questions requires the ability to parse visual observations and correlate them with a variety of knowledge.
no code implementations • 26 Jul 2022 • Yifeng Zhang, Qihan Xuan, Qiyuan Fu, Chen Li
Remarkably, even when frictional drag is low (snake-terrain kinetic friction coefficient of 0. 20), the body must push against the wedge with a pressure 5 times that from body weight to generate sufficient forward propulsion to move forward.
1 code implementation • CVPR 2022 • Yifeng Zhang, Ming Jiang, Qi Zhao
Explainable visual question answering (VQA) models have been developed with neural modules and query-based knowledge incorporation to answer knowledge-requiring questions.
no code implementations • 9 Aug 2021 • Zhuoyi Zhang, Cheng Jiang, Xiya Zhong, Chang Song, Yifeng Zhang
Face anti-spoofing is an important task to protect the security of face recognition.
no code implementations • CVPR 2021 • Yifeng Zhang, Ming Jiang, Qi Zhao
Existing explainable and explicit visual reasoning methods only perform reasoning based on visual evidence but do not take into account knowledge beyond what is in the visual scene.
no code implementations • 27 Dec 2020 • Eric Marberg, Yifeng Zhang
For types BC and D, we prove that these $W$-graphs are dual to each other, a phenomenon which does not occur in type A.
Representation Theory Combinatorics
no code implementations • 27 Jul 2020 • Yifeng Zhang, Ming Jiang, Qi Zhao
At the core of the method is a new Graph Semantic Saliency Network (GraSSNet) that constructs a graph that encodes semantic relationships learned from external knowledge.
no code implementations • 18 Jan 2019 • Yifeng Zhang, Ka-Ho Chow, S. -H. Gary Chan
In this paper, we develop a Depth-Adaptive Long Short-Term Memory (DA-LSTM) architecture, which can dynamically adjust the structure depending on information distribution without prior knowledge.
1 code implementation • 12 Dec 2018 • Eric Marberg, Yifeng Zhang
We study a family of symmetric functions $\hat F_z$ indexed by involutions $z$ in the affine symmetric group.
Combinatorics Representation Theory
no code implementations • CVPR 2019 • Yuan Liu, Lin Ma, Yifeng Zhang, Wei Liu, Shih-Fu Chang
In this paper, we propose a multi-granularity generator (MGG) to perform the temporal action proposal from different granularity perspectives, relying on the video visual features equipped with the position embedding information.
Ranked #2 on
Action Recognition
on THUMOS’14
no code implementations • 20 Nov 2018 • Ka-Ho Chow, Anish Hiranandani, Yifeng Zhang, S. -H. Gary Chan
Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes spatiotemporal characteristics.
no code implementations • 18 Jul 2018 • Yuan Liu, Yuancheng Wang, Nan Li, Xu Cheng, Yifeng Zhang, Yongming Huang, Guojun Lu
We propose an attention-based approach to give a discrimination between texture areas and smooth areas.