b'Thanh-Toan Do'

research

∙ 08/29/2023

Unveiling Camouflage: A Learnable Fourier-based Augmentation for Camouflaged Object Detection and Instance Segmentation

Camouflaged object detection (COD) and camouflaged instance segmentation...

0 Minh-Quan Le, et al. ∙

research

∙ 08/29/2023

Few-Shot Object Detection via Synthetic Features with Optimal Transport

Few-shot object detection aims to simultaneously localize and classify t...

0 Anh-Khoa Nguyen Vu, et al. ∙

research

∙ 06/07/2023

Optimal Transport Model Distributional Robustness

Distributional robustness is a promising framework for training deep lea...

0 Van-Anh Nguyen, et al. ∙

research

∙ 05/31/2023

Noisy-label Learning with Sample Selection based on Noise Rate Estimate

Noisy-labels are challenging for deep learning due to the high capacity ...

0 Arpit Garg, et al. ∙

research

∙ 04/15/2023

Instance-level Few-shot Learning with Class Hierarchy Mining

Few-shot learning is proposed to tackle the problem of scarce training d...

0 Anh-Khoa Nguyen Vu, et al. ∙

research

∙ 04/15/2023

Few-shot Camouflaged Animal Detection and Segmentation

Camouflaged object detection and segmentation is a new and challenging r...

0 Thanh-Danh Nguyen, et al. ∙

research

∙ 03/31/2023

Robotic Perception of Transparent Objects: A Review

Transparent object perception is a rapidly developing research problem i...

0 Jiaqi Jiang, et al. ∙

research

∙ 03/20/2023

PASS: Peer-Agreement based Sample Selection for training with Noisy Labels

Noisy labels present a significant challenge in deep learning because mo...

0 Arpit Garg, et al. ∙

research

∙ 03/09/2023

MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

Few-shot instance segmentation extends the few-shot learning paradigm to...

0 Minh-Quan Le, et al. ∙

research

∙ 02/06/2023

Flat Seeking Bayesian Neural Networks

Bayesian Neural Networks (BNNs) offer a probabilistic interpretation for...

0 Van-Anh Nguyen, et al. ∙

research

∙ 01/04/2023

Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach

Learning from noisy labels plays an important role in the deep learning ...

0 Cuong Nguyen, et al. ∙

research

∙ 01/04/2023

Task Weighting in Meta-learning with Trajectory Optimisation

Developing meta-learning algorithms that are un-biased toward a subset o...

0 Cuong Nguyen, et al. ∙

research

∙ 10/14/2022

Vision Transformer Visualization: What Neurons Tell and How Neurons Behave?

Recently vision transformers (ViT) have been applied successfully for va...

0 Van-Anh Nguyen, et al. ∙

research

∙ 09/02/2022

Instance-Dependent Noisy Label Learning via Graphical Modelling

Noisy labels are unavoidable yet troublesome in the ecosystem of deep le...

0 Arpit Garg, et al. ∙

research

∙ 08/20/2022

Where Shall I Touch? Vision-Guided Tactile Poking for Transparent Object Grasping

Picking up transparent objects is still a challenging task for robots. T...

0 Jiaqi Jiang, et al. ∙

research

∙ 07/11/2022

A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulation

Transparent objects are widely used in our daily lives and therefore rob...

0 Jiaqi Jiang, et al. ∙

research

∙ 11/01/2021

Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification

The shipping industry is an important component of the global trade and ...

0 Manolis Pitsikalis, et al. ∙

research

∙ 06/09/2021

Probabilistic task modelling for meta-learning

We propose probabilistic task modelling – a generative probabilistic mod...

0 Cuong C. Nguyen, et al. ∙

research

∙ 03/31/2021

Camouflaged Instance Segmentation: Dataset and Benchmark Suite

This paper pushes the envelope on camouflaged regions to decompose them ...

0 Trung-Nghia Le, et al. ∙

research

∙ 01/27/2021

Similarity of Classification Tasks

Recent advances in meta-learning has led to remarkable performances on s...

0 Cuong Nguyen, et al. ∙

research

∙ 09/23/2020

Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering

Different approaches have been proposed to Visual Question Answering (VQ...

0 Tuong Do, et al. ∙

research

∙ 09/09/2020

Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding

Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample s...

0 Binh X. Nguyen, et al. ∙

research

∙ 07/25/2020

MirrorNet: Bio-Inspired Adversarial Attack for Camouflaged Object Segmentation

Camouflaged objects are generally difficult to be detected in their natu...

0 Jinnan Yan, et al. ∙

research

∙ 05/15/2020

Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking

The field of Human Activity Recognition (HAR) focuses on obtaining and a...

0 Flávia Alves, et al. ∙

research

∙ 03/05/2020

PAC-Bayesian Meta-learning with Implicit Prior

We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-lea...

0 Cuong Nguyen, et al. ∙

research

∙ 09/26/2019

Compact Trilinear Interaction for Visual Question Answering

In Visual Question Answering (VQA), answers have a great correlation wit...

0 Tuong Do, et al. ∙

research

∙ 09/26/2019

Overcoming Data Limitation in Medical Visual Question Answering

Traditional approaches for Visual Question Answering (VQA) require large...

0 Binh D. Nguyen, et al. ∙

research

∙ 08/01/2019

Scalable Place Recognition Under Appearance Change for Autonomous Driving

A major challenge in place recognition for autonomous driving is to be r...

0 Anh-Dzung Doan, et al. ∙

research

∙ 07/27/2019

Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

We introduce a new, rigorously-formulated Bayesian meta-learning algorit...

4 Cuong Nguyen, et al. ∙

research

∙ 04/26/2019

Bayesian Generative Active Deep Learning

Deep learning models have demonstrated outstanding performance in severa...

0 Toan Tran, et al. ∙

research

∙ 04/24/2019

Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning

Representing images by compact hash codes is an attractive approach for ...

0 Thanh-Toan Do, et al. ∙

research

∙ 04/18/2019

A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning

We propose a method that substantially improves the efficiency of deep d...

44 Thanh-Toan Do, et al. ∙

research

∙ 04/06/2019

SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences

This paper presents a novel randomized algorithm for robust point cloud ...

0 Huu Le, et al. ∙

research

∙ 03/23/2019

V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation

We propose V2CNet, a new deep learning framework to automatically transl...

12 Anh Nguyen, et al. ∙

research

∙ 02/05/2019

SASSE: Scalable and Adaptable 6-DOF Pose Estimation

Visual localization has become a key enabling component of many place re...

14 Huu Le, et al. ∙

research

∙ 01/12/2019

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

As the post-processing step for object detection, non-maximum suppressio...

12 Yu Liu, et al. ∙

research

∙ 11/20/2018

Practical Visual Localization for Autonomous Driving: Why Not Filter?

A major focus of current research on place recognition is visual localiz...

28 Anh-Dzung Doan, et al. ∙

research

∙ 10/24/2018

A Binary Optimization Approach for Constrained K-Means Clustering

K-Means clustering still plays an important role in many computer vision...

8 Huu Le, et al. ∙

research

∙ 07/03/2018

MediaEval 2018: Predicting Media Memorability Task

In this paper, we present the Predicting Media Memorability task, which ...

0 Romain Cohendet, et al. ∙

research

∙ 06/16/2018

G2D: from GTA to Data

This document describes G2D, a software that enables capturing videos fr...

0 Anh-Dzung Doan, et al. ∙

research

∙ 06/04/2018

Bayesian Semantic Instance Segmentation in Open Set World

This paper addresses the instance segmentation task in the open-set cond...

0 Trung Pham, et al. ∙

research

∙ 03/16/2018

Object Captioning and Retrieval with Natural Language

We address the problem of jointly learning vision and language to unders...

0 Anh Nguyen, et al. ∙

research

∙ 02/28/2018

Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image

Detecting objects and their 6D poses from only RGB images is an importan...

0 Thanh-Toan Do, et al. ∙

research

∙ 02/21/2018

Binary Constrained Deep Hashing Network for Image Retrieval without Human Intervention

Learning compact binary codes for image retrieval problem using deep neu...

0 Thanh-Toan Do, et al. ∙

research

∙ 02/19/2018

Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing

The two most important requirements for unsupervised data-dependent hash...

0 Tuan Hoang, et al. ∙

research

∙ 02/10/2018

On-device Scalable Image-based Localization

We present the scalable design of an entire on-device system for large-s...

0 Ngoc-Trung Tran, et al. ∙

research

∙ 02/07/2018

From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval

Convolutional Neural Network (CNN) is a very powerful approach to extrac...

0 Thanh-Toan Do, et al. ∙

research

∙ 12/08/2017

Compact Hash Code Learning with Binary Deep Neural Network

In this work, we firstly propose deep network models and learning algori...

0 Thanh-Toan Do, et al. ∙

research

∙ 11/24/2017

Supervised Hashing with End-to-End Binary Deep Neural Network

Image hashing is a popular technique applied to large scale content-base...

0 Dang-Khoa Le Tan, et al. ∙

research

∙ 09/21/2017

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

We propose AffordanceNet, a new deep learning approach to simultaneously...

0 Thanh-Toan Do, et al. ∙

Thanh-Toan Do

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