Search Results for author: Tin Lun Lam

Found 23 papers, 10 papers with code

Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks

1 code implementation28 Mar 2023 Mingjian Liang, Junjie Hu, Chenyu Bao, Hua Feng, Fuqin Deng, Tin Lun Lam

Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features.

Crowd Counting object-detection +4

Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation

1 code implementation9 Mar 2023 Junjie Hu, Chenyou Fan, Liguang Zhou, Qing Gao, Honghai Liu, Tin Lun Lam

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth.

Autonomous Driving Depth Prediction +2

Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification

no code implementations31 Dec 2022 Liguang Zhou, Yuhongze Zhou, Xiaonan Qi, Junjie Hu, Tin Lun Lam, Yangsheng Xu

Then, to build multi-scale hierarchical information of input features, we utilize an attention fusion mechanism to aggregate features from multiple layers of the backbone network.

Scene Classification Scene Recognition +1

Peer Learning for Unbiased Scene Graph Generation

no code implementations31 Dec 2022 Liguang Zhou, Junjie Hu, Yuhongze Zhou, Tin Lun Lam, Yangsheng Xu

Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image.

Graph Generation Unbiased Scene Graph Generation

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

1 code implementation29 Aug 2022 Junjie Hu, Chenyou Fan, Mete Ozay, Hua Feng, Yuan Gao, Tin Lun Lam

In this paper, we introduce the ground-to-aerial perception knowledge transfer and propose a progressive semi-supervised learning framework that enables drone perception using only labeled data of ground viewpoint and unlabeled data of flying viewpoints.

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

We study data-free knowledge distillation (KD) for monocular depth estimation (MDE), which learns a lightweight model for real-world depth perception tasks by compressing it from a trained teacher model while lacking training data in the target domain.

Image Classification Knowledge Distillation +1

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

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

Specifically, we propose to integrate the mixture of experts with a divide and ensemble strategy to remedy the severely long-tailed distribution of predicate classes, which is applicable to the majority of unbiased scene graph generators.

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

Boosting Light-Weight Depth Estimation Via Knowledge Distillation

2 code implementations13 May 2021 Junjie Hu, Chenyou Fan, Hualie Jiang, Xiyue Guo, Yuan Gao, Xiangyong Lu, Tin Lun Lam

However, this KD process can be challenging and insufficient due to the large model capacity gap between the teacher and the student.

Knowledge Distillation Monocular Depth Estimation

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.

Foreground Segmentation Image Matting +1

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

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 +1

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