Search Results for author: Iman Abbasnejad

Found 6 papers, 0 papers with code

Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning

no code implementations CVPR 2020 Ehsan Abbasnejad, Iman Abbasnejad, Qi Wu, Javen Shi, Anton Van Den Hengel

For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed.

Visual Dialog

Meta Transfer Learning for Facial Emotion Recognition

no code implementations25 May 2018 Dung Nguyen, Kien Nguyen, Sridha Sridharan, Iman Abbasnejad, David Dean, Clinton Fookes

The use of deep learning techniques for automatic facial expression recognition has recently attracted great interest but developed models are still unable to generalize well due to the lack of large emotion datasets for deep learning.

Facial Emotion Recognition Facial Expression Recognition +1

Bayesian Conditional Generative Adverserial Networks

no code implementations17 Jun 2017 M. Ehsan Abbasnejad, Qinfeng Shi, Iman Abbasnejad, Anton Van Den Hengel, Anthony Dick

Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish.

Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes

no code implementations13 Jun 2017 Iman Abbasnejad, Sridha Sridharan, Simon Denman, Clinton Fookes, Simon Lucey

In this paper the problem of complex event detection in the continuous domain (i. e. events with unknown starting and ending locations) is addressed.

Action Recognition Event Detection

Learning Temporal Alignment Uncertainty for Efficient Event Detection

no code implementations4 Sep 2015 Iman Abbasnejad, Sridha Sridharan, Simon Denman, Clinton Fookes, Simon Lucey

A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence.

Event Detection

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