Search Results for author: Tin Lun Lam

Found 19 papers, 6 papers with code

Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer

no code implementations29 Aug 2022 Junjie Hu, Chenyou Fan, Hua Feng, Yuan Gao, Tin Lun Lam

We study a practical yet hasn't been explored problem: how a drone can perceive in an environment from viewpoints of different flight heights.

Autonomous Driving Knowledge Distillation +1

Data-free Dense Depth Distillation

no code implementations26 Aug 2022 Junjie Hu, Chenyou Fan, Mete Ozay, Hualie Jiang, Tin Lun Lam

To tackle the second difficulty, we propose to utilize a transformation network that efficiently learns to fit the simulated data to the feature distribution of the teacher model.

Knowledge Distillation Monocular Depth Estimation

Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation

no code implementations15 Aug 2022 Liguang Zhou, Yuhongze Zhou, Tin Lun Lam, Yangsheng Xu

These methods often design the sophisticated context-encoder to extract the inherent relevance of scene context w. r. t the intrinsic predicates and complicated networks to improve the learning capabilities of the network model for highly imbalanced data distributions.

Graph Generation object-detection +2

Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks

no code implementations5 Aug 2022 Jingtao Tang, Yuan Gao, Tin Lun Lam

In this paper, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources.

Multi-agent Reinforcement Learning

Deep Depth Completion from Extremely Sparse Data: A Survey

no code implementations11 May 2022 Junjie Hu, Chenyu Bao, Mete Ozay, Chenyou Fan, Qing Gao, Honghai Liu, Tin Lun Lam

Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e. g., LiDARs.

3D Reconstruction Autonomous Driving +2

Abnormal Occupancy Grid Map Recognition using Attention Network

1 code implementation18 Oct 2021 Fuqin Deng, Hua Feng, Mingjian Liang, Qi Feng, Ningbo Yi, Yong Yang, Yuan Gao, Junfeng Chen, Tin Lun Lam

The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it.

View Blind-spot as Inpainting: Self-Supervised Denoising with Mask Guided Residual Convolution

no code implementations10 Sep 2021 Yuhongze Zhou, Liguang Zhou, Tin Lun Lam, Yangsheng Xu

Our MGRConv can be regarded as soft partial convolution and find a trade-off among partial convolution, learnable attention maps, and gated convolution.


Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth

no code implementations8 Sep 2021 Chongyang Wang, Yuan Gao, Chenyou Fan, Junjie Hu, Tin Lun Lam, Nicholas D. Lane, Nadia Bianchi-Berthouze

For such issues, we propose a novel Learning to Agreement (Learn2Agree) framework to tackle the challenge of learning from multiple annotators without objective ground truth.

AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments

no code implementations3 Aug 2021 Tianwei Zhang, Huayan Zhang, Xiaofei Li, Junfeng Chen, Tin Lun Lam, Sethu Vijayakumar

Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches.

Depth Estimation Simultaneous Localization and Mapping

PoseFusion2: Simultaneous Background Reconstruction and Human Shape Recovery in Real-time

no code implementations2 Aug 2021 Huayan Zhang, Tianwei Zhang, Tin Lun Lam, Sethu Vijayakumar

Dynamic environments that include unstructured moving objects pose a hard problem for Simultaneous Localization and Mapping (SLAM) performance.

Pose Estimation Simultaneous Localization and Mapping

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

1 code implementation1 Aug 2021 Liguang Zhou, Jun Cen, Xingchao Wang, Zhenglong Sun, Tin Lun Lam, Yangsheng Xu

First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene.

Scene Recognition

Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition

1 code implementation1 Aug 2021 Bo Miao, Liguang Zhou, Ajmal Mian, Tin Lun Lam, Yangsheng Xu

The final results in this work show that OTS successfully extracts object features and learns object relations from the segmentation network.

Scene Recognition

Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention

no code implementations31 Mar 2021 Yuhongze Zhou, Liguang Zhou, Tin Lun Lam, Yangsheng Xu

This paper presents a semantic-guided automatic natural image matting pipeline with Trimap Generation Network and light-weight non-local attention, which does not need trimap and background as input.

Image Matting

A Two-stage Unsupervised Approach for Low light Image Enhancement

no code implementations19 Oct 2020 Junjie Hu, Xiyue Guo, Junfeng Chen, Guanqi Liang, Fuqin Deng, Tin Lun Lam

However, most of them suffer from the following problems: 1) the need of pairs of low light and normal light images for training, 2) the poor performance for dark images, 3) the amplification of noise.

Low-Light Image Enhancement Simultaneous Localization and Mapping

Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment

3 code implementations19 Oct 2020 Xiyue Guo, Junjie Hu, Junfeng Chen, Fuqin Deng, Tin Lun Lam

The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL).

Graph Matching Simultaneous Localization and Mapping

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