Search Results for author: Anqi Xu

Found 10 papers, 3 papers with code

An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing

no code implementations CVPR 2024 Feiran Hu, Chenlin Zhang, Jiangliang Guo, Xiu-Shen Wei, Lin Zhao, Anqi Xu, Lingyan Gao

In this paper we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects.

Contrastive Learning Self-Supervised Learning

Delving Deep into Simplicity Bias for Long-Tailed Image Recognition

no code implementations7 Feb 2023 Xiu-Shen Wei, Xuhao Sun, Yang shen, Anqi Xu, Peng Wang, Faen Zhang

Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks.

Long-tail Learning Self-Supervised Learning

Exploration strategies for articulatory synthesis of complex syllable onsets

1 code implementation20 Apr 2022 Daniel R. van Niekerk, Anqi Xu, Branislav Gerazov, Paul K. Krug, Peter Birkholz, Yi Xu

High-quality articulatory speech synthesis has many potential applications in speech science and technology.

Speech Synthesis

Lifelong Topological Visual Navigation

no code implementations16 Oct 2021 Rey Reza Wiyatno, Anqi Xu, Liam Paull

Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space through a topological map.

Visual Navigation

Evaluating Features and Metrics for High-Quality Simulation of Early Vocal Learning of Vowels

no code implementations20 May 2020 Branislav Gerazov, Daniel van Niekerk, Anqi Xu, Paul Konstantin Krug, Peter Birkholz, Yi Xu

One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target.

Speech Synthesis

Adversarial Examples in Modern Machine Learning: A Review

1 code implementation13 Nov 2019 Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, Archy de Berker

Recent research has found that many families of machine learning models are vulnerable to adversarial examples: inputs that are specifically designed to cause the target model to produce erroneous outputs.

Adversarial Attack BIG-bench Machine Learning

Physical Adversarial Textures that Fool Visual Object Tracking

no code implementations ICCV 2019 Rey Reza Wiyatno, Anqi Xu

We present a system for generating inconspicuous-looking textures that, when displayed in the physical world as digital or printed posters, cause visual object tracking systems to become confused.

Object Visual Object Tracking

Hinted Networks

no code implementations15 Dec 2018 Joel Lamy-Poirier, Anqi Xu

We present Hinted Networks: a collection of architectural transformations for improving the accuracies of neural network models for regression tasks, through the injection of a prior for the output prediction (i. e. a hint).

Camera Relocalization Indoor Localization +1

Maximal Jacobian-based Saliency Map Attack

no code implementations23 Aug 2018 Rey Wiyatno, Anqi Xu

The Jacobian-based Saliency Map Attack is a family of adversarial attack methods for fooling classification models, such as deep neural networks for image classification tasks.

Adversarial Attack Classification +2

Underwater Multi-Robot Convoying using Visual Tracking by Detection

1 code implementation25 Sep 2017 Florian Shkurti, Wei-Di Chang, Peter Henderson, Md Jahidul Islam, Juan Camilo Gamboa Higuera, Jimmy Li, Travis Manderson, Anqi Xu, Gregory Dudek, Junaed Sattar

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments.

object-detection Object Detection +1

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