59 citations · 240 across the 22 of their papers we have counts for
25 papers
VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions
Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh +10
The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds. This transition introduces an ``extreme cold-start'' problem: unli…
Agentic Personalized Fashion Recommendation in the Age of Generative AI: Challenges, Opportunities, and Evaluation
Yashar Deldjoo, Nima Rafiee, Mahdyar Ravanbakhsh
Fashion recommender systems (FaRS) face distinct challenges due to rapid trend shifts, nuanced user preferences, intricate item-item compatibility, and the complex interplay among…
LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing
Leonard Hackel, Kai Norman Clasen, Mahdyar Ravanbakhsh +1
Visual question answering (VQA) methods in remote sensing (RS) aim to answer natural language questions with respect to an RS image. Most of the existing methods require a large am…
Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images
Tom-Lukas Breitkopf, Leonard W. Hackel, Mahdyar Ravanbakhsh +4
Subsurface tile drainage pipes provide agronomic, economic and environmental benefits. By lowering the water table of wet soils, they improve the aeration of plant roots and ultima…
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing
Tim Siebert, Kai Norman Clasen, Mahdyar Ravanbakhsh +1
With the new generation of satellite technologies, the archives of remote sensing (RS) images are growing very fast. To make the intrinsic information of each RS image easily acces…
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification
Tom Burgert, Mahdyar Ravanbakhsh, Begüm Demir
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. To address MLC problems,…
Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
The development of cross-modal retrieval systems that can search and retrieve semantically relevant data across different modalities based on a query in any modality has attracted…
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
The development of accurate and scalable cross-modal image-text retrieval methods, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g.…
Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
Due to the availability of large-scale multi-modal data (e.g., satellite images acquired by different sensors, text sentences, etc) archives, the development of cross-modal retriev…
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels
Ahmet Kerem Aksoy, Mahdyar Ravanbakhsh, Tristan Kreuziger +1
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in r…
Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval
Gencer Sumbul, Mahdyar Ravanbakhsh, Begüm Demir
Learning the similarity between remote sensing (RS) images forms the foundation for content-based RS image retrieval (CBIR). Recently, deep metric learning approaches that map the…
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification
Ahmet Kerem Aksoy, Mahdyar Ravanbakhsh, Begüm Demir
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on…
S2-cGAN: Self-Supervised Adversarial Representation Learning for Binary Change Detection in Multispectral Images
Jose Luis Holgado Alvarez, Mahdyar Ravanbakhsh, Begüm Demir
Deep Neural Networks have recently demonstrated promising performance in binary change detection (CD) problems in remote sensing (RS), requiring a large amount of labeled multitemp…
Learning Self-Awareness for Autonomous Vehicles: Exploring Multisensory Incremental Models
Mahdyar Ravanbakhsh, Mohamad Baydoun, Damian Campo +4
The technology for autonomous vehicles is close to replacing human drivers by artificial systems endowed with high-level decision-making capabilities. In this regard, systems must…
Uncertainty-Driven Semantic Segmentation through Human-Machine Collaborative Learning
Mahdyar Ravanbakhsh, Tassilo Klein, Kayhan Batmanghelich +1
Deep learning-based approaches achieve state-of-the-art performance in the majority of image segmentation benchmarks. However, training of such models requires a sizable amount of…
Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series
Mahdyar Ravanbakhsh
Novelty detection is a process for distinguishing the observations that differ in some respect from the observations that the model is trained on. Novelty detection is one of the f…
Hierarchy of GANs for learning embodied self-awareness model
Mahdyar Ravanbakhsh, Mohamad Baydoun, Damian Campo +4
In recent years several architectures have been proposed to learn embodied agents complex self-awareness models. In this paper, dynamic incremental self-awareness (SA) models are p…
Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving
Mahdyar Ravanbakhsh, Mohamad Baydoun, Damian Campo +4
This paper presents a novel approach for learning self-awareness models for autonomous vehicles. The proposed technique is based on the availability of synchronized multi-sensor dy…
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo +5
This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used t…
Abnormal Event Detection in Videos using Generative Adversarial Nets
Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto +3
In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corres…
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds
Mahdyar Ravanbakhsh, Enver Sangineto, Moin Nabi +1
Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios. However, the ambiguity and the lack of sufficient abnormal groun…
Efficient Convolutional Neural Network with Binary Quantization Layer
Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi +2
In this paper we introduce a novel method for segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and sem…
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi +2
Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have show…
CNN-aware Binary Map for General Semantic Segmentation
Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi +2
In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes…