I received my PhD in Computer Science from University Of Central Florida Center for Research in Computer Vision (CRCV)
under supervision of Dr. Mubarak Shah .
My main research area is computer vision and machine learning specially saliency detection and semantic segmentation.
Before that, I had graduated in Master degree of Artificial Intelligence in Amirkabir University of Science and Technology in Tehran - Iran.
Semi Supervised Semantic Segmentation Using Generative Adversarial Network
Semantic segmentation has been a long standing challenging
task in computer vision. It aims at assigning a label
to each image pixel and needs significant number of pixellevel
annotated data, which is often unavailable. To address
this lack, in this paper, we leverage, on one hand, massive
amount of available unlabeled or weakly labeled data, and
on the other hand, non-real images created through Generative
Adversarial Networks. In particular, we propose
a semi-supervised framework – based on Generative Adversarial
Networks (GANs) – which consists of a generator
network to provide extra training examples to a multi-class
classifier, acting as discriminator in the GAN framework,
that assigns sample a label y from the K possible classes or
marks it as a fake sample (extra class). The underlying idea
is that adding large fake visual data forces real samples to
be close in the feature space, enabling a bottom-up clustering
process, which, in turn, improves multiclass pixel classification.
To ensure higher quality of generated images for
GANs with consequent improved pixel classification, we extend
the above framework by adding weakly annotated data,
i.e., we provide class level information to the generator. We
tested our approaches on several challenging benchmarking
visual datasets, i.e. PASCAL, SiftFLow, Stanford and
CamVid, achieving competitive performance also compared
to state-of-the-art semantic segmentation methods.
[ Paper (Published in ICCV17)]
[ Presentation file]
Deep Learning Human Mind for Automated Visual Classification
What if we could effectively read the mind and transfer human
visual capabilities to computer vision methods? In this paper, we
aim at addressing this question by developing the first visual object
classifier driven by human brain signals. In particular, we employ EEG
data evoked by visual object stimuli combined with Recurrent Neural
Networks (RNN) to learn a discriminative brain activity manifold of
visual categories. Afterward, we train a Convolutional Neural Network
(CNN)–based regressor to project images onto the learned manifold, thus
effectively allowing machines to employ human brain–based features
for automated visual classification. We use a 32-channel EEG to
record brain activity of seven subjects while looking at images of
40 ImageNet object classes. The proposed RNN-based approach for
discriminating object classes using brain signals reaches an average
accuracy of about 40%, which outperforms existing methods attempting
to learn EEG visual object representations. As for automated object
categorization, our human brain–driven approach obtains competitive
performance, comparable to those achieved by powerful CNN models,
both on ImageNet and CalTech 101, thus demonstrating its classification
and generalization capabilities. This gives us a real hope that, indeed,
human mind can be read and transferred to machines.
Scene Labeling Using Sparse Precision Matrix
Scene labeling task is to segment the image into meaningful
regions and categorize them into classes of objects
which comprised the image. Commonly used methods typically
find the local features for each segment and label them
using classifiers. Afterwards, labeling is smoothed in order
to make sure that neighboring regions receive similar labels.
However, these methods ignore expressive connections
between labels and non-local dependencies among regions.
In this paper, we propose to use a sparse estimation of precision
matrix (also called concentration matrix), which is
the inverse of covariance matrix of data obtained by graphical
lasso to find interaction between labels and regions. To
do this, we formulate the problem as an energy minimization
over a graph, whose structure is captured by applying
sparse constraint on the elements of the precision matrix.
This graph encodes (or represents) only significant interactions
and avoids a fully connected graph, which is typically
used to reflect the long distance associations. We use local
and global information to achieve better labeling. We assess
our approach on three datasets and obtained promising
[CVPR 2016 Paper]
[ Presentation file]
Scene Labeling Through Knowledge-Based Rules
Employing Constrained Integer Linear Programing
Scene labeling entails segmentation of an image into meaningful regions or superpixels
and their categorization into classes of objects composing the image. Commonly
used methods typically find the local features for each segment and label them using
classifiers. Afterward, labeling is smoothed locally in order to make sure that neighboring
regions receive similar labels. However, these methods ignore expressive and
non-local dependencies among regions as they require expensive training and inference.
In this paper, we propose to use high level knowledge regarding rules- such as
presence, implication and mutual exclusivity- in the inference to incorporate dependencies
among regions in the image to improve scores of scene labeling. Towards this
aim, we extract knowledge-based rules from training data and transform them into constraints
in Integer Programming to optimize the structured problem of assigning labels
to super-pixels (consequently pixels) in an image. In addition, we propose to use softconstraints,
which permit violation of hard constraints by imposing penalties, and can
be solved through slack variables; thereby yielding a more flexible model. Furthermore,
we learn scene-label association weights in order to employ global context to
improve label confidences. In doing so, we use a deep neural network trained to obtain
scene categories in an image, and we learn associations of image-level information
with labels of super-pixel through a non-negative sparse regression model. We assess
our approach on three datasets and obtain promising results.
Visual Saliency Detection Using Group Lasso Regularization in Videos of Natural Scenes
Visual saliency is the ability of a vision system
to promptly select the most relevant data in the scene and
reduce the amount of visual data that needs to be processed.
Thus, its applications for complex tasks such as object detection,
object recognition and video compression have attained
interest in computer vision studies. In this paper,we introduce
a novel unsupervised method for detecting visual saliency
in videos of natural scenes. For this, we divide a video
into non-overlapping cuboids and create a matrix whose
columns correspond to intensity values of these cuboids.
Simultaneously, we segment the video using a hierarchical
segmentation method and obtain super-voxels. A dictionary
learned from the feature data matrix of the video is subsequently
used to represent the video as coefficients of atoms.
Then, these coefficients are decomposed into salient and nonsalient
parts. We propose to use group lasso regularization
to find the sparse representation of a video, which benefits
from grouping information provided by super-voxels and
extracted features from the cuboids.We find saliency regions
by decomposing the feature matrix of a video into low-rank
and sparse matrices by using robust principal component
analysis matrix recovery method. The applicability of our
method is tested on four video data sets of natural scenes.
Our experiments provide promising results in terms of predicting
eye movement using standard evaluation methods.
In addition, we show our video saliency can be used to improve
the performance of human action recognition on a standard dataset.
[Related Publication ]
Covariance of Motion and Appearance Features for Human Action and Gesture Recognition
In this work, we introduce a novel descriptor for general purpose video analysis. In our approach, we
compute kinematic features from optical flow and first and second-order derivatives of intensities to represent motion and appearance
respectively. These features are then used to construct covariance matrices which capture joint statistics of both low-level motion
and appearance features extracted from a video. Using an over-complete dictionary of the covariance based descriptors built from
labeled training samples, we formulate low-level event recognition as a sparse linear approximation problem. Within this, we pose the
sparse decomposition of a covariance matrix, which also conforms to the space of semi-positive definite matrices, as a determinant
maximization problem. Also since covariance matrices lie on non-linear Riemannian manifolds, we compare our former approach with
a sparse linear approximation alternative that is suitable for equivalent vector spaces of covariance matrices. This is done by searching
for the best projection of the query data on a dictionary using an Orthogonal Matching pursuit algorithm.
We show the applicability of our video descriptor in two different application domains - namely low-level event recognition in
unconstrained scenarios and gesture recognition using one shot learning. Our experiments provide promising insights in large scale
UCF is a part of SRI-Sarnoff team and this work was part of ALADDIN projects.