Search Results for author: Omid Mohamad Nezami

Found 8 papers, 4 papers with code

Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection

1 code implementation5 Jun 2020 Omid Mohamad Nezami, Akshay Chaturvedi, Mark Dras, Utpal Garain

We specifically aim to attack the widely used Faster R-CNN by changing the predicted label for a particular object in an image: where prior work has targeted one specific object (a stop sign), we generalise to arbitrary objects, with the key challenge being the need to change the labels of all bounding boxes for all instances of that object type.

Adversarial Attack Image Captioning +5

Towards Generating Stylized Image Captions via Adversarial Training

no code implementations8 Aug 2019 Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris, Len Hamey

While most image captioning aims to generate objective descriptions of images, the last few years have seen work on generating visually grounded image captions which have a specific style (e. g., incorporating positive or negative sentiment).

Image Captioning

Image Captioning using Facial Expression and Attention

no code implementations8 Aug 2019 Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris

An analysis of the generated captions finds that, perhaps unexpectedly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.

Caption Generation Image Captioning

ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection

4 code implementations4 Jun 2019 Omid Mohamad Nezami, Paria Jamshid Lou, Mansoureh Karami

This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO).

Senti-Attend: Image Captioning using Sentiment and Attention

no code implementations24 Nov 2018 Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris

However, such models typically have difficulty in balancing the semantic aspects of the image and the non-factual dimensions of the caption; in addition, it can be observed that humans may focus on different aspects of an image depending on the chosen sentiment or style of the caption.

Image Captioning

Automatic Recognition of Student Engagement using Deep Learning and Facial Expression

3 code implementations7 Aug 2018 Omid Mohamad Nezami, Mark Dras, Len Hamey, Deborah Richards, Stephen Wan, Cecile Paris

This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data.

Facial Expression Recognition Facial Expression Recognition (FER)

Face-Cap: Image Captioning using Facial Expression Analysis

1 code implementation6 Jul 2018 Omid Mohamad Nezami, Mark Dras, Peter Anderson, Len Hamey

In this work, we present two variants of our Face-Cap model, which embed facial expression features in different ways, to generate image captions.

Descriptive Image Captioning

Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)

no code implementations2 Jul 2018 Anvar Bahrampour, Omid Mohamad Nezami

Premature convergence in particle swarm optimization (PSO) algorithm usually leads to gaining local optimum and preventing from surveying those regions of solution space which have optimal points in.

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