Search Results for author: Pei Yu

Found 14 papers, 4 papers with code

Should All Proposals be Treated Equally in Object Detection?

1 code implementation7 Jul 2022 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Pei Yu, Jing Yin, Lu Yuan, Zicheng Liu, Nuno Vasconcelos

We formulate this as a learning problem where the goal is to assign operators to proposals, in the detection head, so that the total computational cost is constrained and the precision is maximized.

Object Object Detection

A Token-level Contrastive Framework for Sign Language Translation

1 code implementation11 Apr 2022 Biao Fu, PeiGen Ye, Liang Zhang, Pei Yu, Cong Hu, Yidong Chen, Xiaodong Shi

Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people.

Contrastive Learning Machine Translation +5

The Overlooked Classifier in Human-Object Interaction Recognition

no code implementations10 Mar 2022 Ying Jin, Yinpeng Chen, Lijuan Wang, JianFeng Wang, Pei Yu, Lin Liang, Jenq-Neng Hwang, Zicheng Liu

Human-Object Interaction (HOI) recognition is challenging due to two factors: (1) significant imbalance across classes and (2) requiring multiple labels per image.

Classification Human-Object Interaction Detection +4

SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering

no code implementations25 Jan 2022 Peixi Xiong, Quanzeng You, Pei Yu, Zicheng Liu, Ying Wu

As a multi-modality task, it is challenging since it requires not only visual and textual understanding, but also the ability to align cross-modality representations.

Question Answering Visual Question Answering

Improving Vision Transformers for Incremental Learning

no code implementations12 Dec 2021 Pei Yu, Yinpeng Chen, Ying Jin, Zicheng Liu

This paper proposes a working recipe of using Vision Transformer (ViT) in class incremental learning.

Class Incremental Learning Incremental Learning

A Unified Transferable Model for ML-Enhanced DBMS

1 code implementation6 May 2021 Ziniu Wu, Pei Yu, Peilun Yang, Rong Zhu, Yuxing Han, Yaliang Li, Defu Lian, Kai Zeng, Jingren Zhou

We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.


Beating the Standard Quantum Limit under Ambient Conditions with Solid-State Spins

no code implementations28 Jan 2021 Tianyu Xie, Zhiyuan Zhao, Xi Kong, Wenchao Ma, Mengqi Wang, Xiangyu Ye, Pei Yu, Zhiping Yang, Shaoyi Xu, Pengfei Wang, Ya Wang, Fazhan Shi, Jiangfeng Du

However, it has not been realized in solid-state spin systems at ambient conditions, owing to its intrinsic complexity for the preparation and survival of pure and entangled quantum states.

Quantum Physics

Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning

1 code implementation17 Nov 2019 Fuxun Yu, Di Wang, Yinpeng Chen, Nikolaos Karianakis, Tong Shen, Pei Yu, Dimitrios Lymberopoulos, Sidi Lu, Weisong Shi, Xiang Chen

In this work, we show that such adversarial-based methods can only reduce the domain style gap, but cannot address the domain content distribution gap that is shown to be important for object detectors.

Object object-detection +2

Deeply Learned Compositional Models for Human Pose Estimation

no code implementations ECCV 2018 Wei Tang, Pei Yu, Ying Wu

This results in a network with a hierarchical compositional architecture and bottom-up/top-down inference stages.

Pose Estimation

Efficient Online Local Metric Adaptation via Negative Samples for Person Re-Identification

no code implementations ICCV 2017 Jiahuan Zhou, Pei Yu, Wei Tang, Ying Wu

In contrast to these methods, this paper advocates a different paradigm: part of the learning can be performed online but with nominal costs, so as to achieve online metric adaptation for different input probes.

Metric Learning Person Re-Identification

Learning Reconstruction-Based Remote Gaze Estimation

no code implementations CVPR 2016 Pei Yu, Jiahuan Zhou, Ying Wu

A common treatment is to use the same local reconstruction in the two spaces, i. e., the reconstruction weights in the appearance space are transferred to the gaze space for gaze reconstruction.

Gaze Estimation

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