Search Results for author: Shuo Gu

Found 7 papers, 1 papers with code

A Unified Knowledge-Distillation and Semi-Supervised Learning Framework to Improve Industrial Ads Delivery Systems

no code implementations5 Feb 2025 Hamid Eghbalzadeh, Yang Wang, Rui Li, Yuji Mo, Qin Ding, Jiaxiang Fu, Liang Dai, Shuo Gu, Nima Noorshams, Sem Park, Bo Long, Xue Feng

Industrial ads ranking systems conventionally rely on labeled impression data, which leads to challenges such as overfitting, slower incremental gain from model scaling, and biases due to discrepancies between training and serving data.

SGNet: Salient Geometric Network for Point Cloud Registration

no code implementations12 Sep 2023 Qianliang Wu, Yaqing Ding, Lei Luo, Haobo Jiang, Shuo Gu, Chuanwei Zhou, Jin Xie, Jian Yang

These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify key correspondences for successful registration.

Point Cloud Registration

Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance

1 code implementation24 Aug 2023 Wei Xie, Haobo Jiang, Shuo Gu, Jin Xie

Robust obstacle avoidance is one of the critical steps for successful goal-driven indoor navigation tasks. Due to the obstacle missing in the visual image and the possible missed detection issue, visual image-based obstacle avoidance techniques still suffer from unsatisfactory robustness.

Center-Based Decoupled Point-cloud Registration for 6D Object Pose Estimation

no code implementations ICCV 2023 Haobo Jiang, Zheng Dang, Shuo Gu, Jin Xie, Mathieu Salzmann, Jian Yang

Our method decouples the translation from the entire transformation by predicting the object center and estimating the rotation in a center-aware manner.

6D Pose Estimation using RGB Object +2

Semantics-Guided Moving Object Segmentation with 3D LiDAR

no code implementations6 May 2022 Shuo Gu, Suling Yao, Jian Yang, Hui Kong

Instead of segmenting the moving objects directly, the network conducts single-scan-based semantic segmentation and multiple-scan-based moving object segmentation in turn.

Object Segmentation +1

Transformationally Identical and Invariant Convolutional Neural Networks through Symmetric Element Operators

no code implementations10 Jun 2018 Shih Chung B. Lo, Matthew T. Freedman, Seong K. Mun, Shuo Gu

We further found that any CNN possessing the same TI kernel property for all convolution layers followed by a flatten layer with weight sharing among their transformation corresponding elements would output the same result for all transformation versions of the original input vector.

Data Augmentation

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