Search Results for author: Muhammad Awais

Found 17 papers, 5 papers with code

MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning

no code implementations30 Nov 2021 Sara Atito, Muhammad Awais, Ammarah Farooq, ZhenHua Feng, Josef Kittler

In this aspect the proposed SSL frame-work MC-SSL0. 0 is a step towards Multi-Concept Self-Supervised Learning (MC-SSL) that goes beyond modelling single dominant label in an image to effectively utilise the information from all the concepts present in it.

Image Classification Self-Supervised Learning +1

Global Interaction Modelling in Vision Transformer via Super Tokens

no code implementations25 Nov 2021 Ammarah Farooq, Muhammad Awais, Sara Ahmed, Josef Kittler

Hence, most of the learning is independent of the image patches $(N)$ in the higher layers, and the class embedding is learned solely based on the Super tokens $(N/M^2)$ where $M^2$ is the window size.

Image Classification Representation Learning

MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps

no code implementations NeurIPS 2021 Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li

First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.

Transfer Learning

Adversarial Robustness for Unsupervised Domain Adaptation

no code implementations ICCV 2021 Muhammad Awais, Fengwei Zhou, Hang Xu, Lanqing Hong, Ping Luo, Sung-Ho Bae, Zhenguo Li

Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.

Adversarial Robustness Unsupervised Domain Adaptation

SiT: Self-supervised vIsion Transformer

2 code implementations8 Apr 2021 Sara Atito, Muhammad Awais, Josef Kittler

We also observed that SiT is good for few shot learning and also showed that it is learning useful representation by simply training a linear classifier on top of the learned features from SiT.

Few-Shot Learning Self-Supervised Learning

NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition

no code implementations5 Mar 2021 Syed Safwan Khalid, Muhammad Awais, Chi-Ho Chan, ZhenHua Feng, Ammarah Farooq, Ali Akbari, Josef Kittler

One key ingredient of DCNN-based FR is the appropriate design of a loss function that ensures discrimination between various identities.

Face Recognition

A Flatter Loss for Bias Mitigation in Cross-dataset Facial Age Estimation

no code implementations20 Oct 2020 Ali Akbari, Muhammad Awais, Zhen-Hua Feng, Ammarah Farooq, Josef Kittler

Compared with existing loss functions, the lower gradient of the proposed loss function leads to the convergence of SGD to a better optimum point, and consequently a better generalisation.

Age Estimation

Deep Convolutional Neural Network Ensembles using ECOC

no code implementations7 Sep 2020 Sara Atito Ali Ahmed, Cemre Zor, Berrin Yanikoglu, Muhammad Awais, Josef Kittler

Deep neural networks have enhanced the performance of decision making systems in many applications including image understanding, and further gains can be achieved by constructing ensembles.

Decision Making

Towards an Adversarially Robust Normalization Approach

1 code implementation19 Jun 2020 Muhammad Awais, Fahad Shamshad, Sung-Ho Bae

In this paper, we investigate how BatchNorm causes this vulnerability and proposed new normalization that is robust to adversarial attacks.

A Convolutional Baseline for Person Re-Identification Using Vision and Language Descriptions

no code implementations20 Feb 2020 Ammarah Farooq, Muhammad Awais, Fei Yan, Josef Kittler, Ali Akbari, Syed Safwan Khalid

However, in real-world surveillance scenarios, frequently no visual information will be available about the queried person.

Person Re-Identification

Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising

no code implementations29 Nov 2018 Fahad Shamshad, Muhammad Awais, Muhammad Asim, Zain ul Aabidin Lodhi, Muhammad Umair, Ali Ahmed

Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise.

Denoising

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

6 code implementations CVPR 2018 Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiao-Jun Wu

We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs).

 Ranked #1 on Face Alignment on 300W (NME_inter-pupil (%, Common) metric)

Data Augmentation Face Alignment

Medical Image Analysis using Convolutional Neural Networks: A Review

no code implementations4 Sep 2017 Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum, Muhammad Awais, Majdi Alnowami, Muhammad Khurram Khan

Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features.

Anomaly Detection

3D Morphable Models as Spatial Transformer Networks

1 code implementation23 Aug 2017 Anil Bas, Patrik Huber, William A. P. Smith, Muhammad Awais, Josef Kittler

In this paper, we show how a 3D Morphable Model (i. e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network.

Cannot find the paper you are looking for? You can Submit a new open access paper.