Search Results for author: Shitao Tang

Found 5 papers, 4 papers with code

Learning Camera Localization via Dense Scene Matching

1 code implementation CVPR 2021 Shitao Tang, Chengzhou Tang, Rui Huang, Siyu Zhu, Ping Tan

We present a new method for scene agnostic camera localization using dense scene matching (DSM), where a cost volume is constructed between a query image and a scene.

Camera Localization

Channel Equilibrium Networks for Learning Deep Representation

1 code implementation ICML 2020 Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo

Unlike prior arts that simply removed the inhibited channels, we propose to "wake them up" during training by designing a novel neural building block, termed Channel Equilibrium (CE) block, which enables channels at the same layer to contribute equally to the learned representation.

Learning Efficient Detector with Semi-supervised Adaptive Distillation

1 code implementation2 Jan 2019 Shitao Tang, Litong Feng, Wenqi Shao, Zhanghui Kuang, Wei zhang, Yimin Chen

ADL enlarges the distillation loss for hard-to-learn and hard-to-mimic samples and reduces distillation loss for the dominant easy samples, enabling distillation to work on the single-stage detector first time, even if the student and the teacher are identical.

Image Classification Knowledge Distillation +1

Fast Video Shot Transition Localization with Deep Structured Models

3 code implementations13 Aug 2018 Shitao Tang, Litong Feng, Zhangkui Kuang, Yimin Chen, Wei zhang

In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos.

Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation

no code implementations24 Jul 2017 Shitao Tang, Yichen Pan

This paper presents a novel ensemble framework to extract highly discriminative feature representation of image and its application for group-level happpiness intensity prediction in wild.

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