35 citations · 72 across the 25 of their papers we have counts for
26 papers
Towards Effective Waste Segmentation for Automated Waste Recycling in Cluttered Background
Mamoona Javaid, Mubashir Noman, Abdul Hannan +3
Rapid expansion of urban areas and population growth is causing an immense increase in waste production, which demands the need for efficient and automated waste management. In thi…
EoCD: Encoder only Remote Sensing Change Detection
Mubashir Noman, Mustansar Fiaz, Hiyam Debary +4
Being a cornerstone of temporal analysis, change detection has been playing a pivotal role in modern earth observation. Existing change detection methods rely on the Siamese encode…
OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents
Akashah Shabbir, Muhammad Umer Sheikh, Muhammad Akhtar Munir +8
Recent progress in multimodal reasoning has enabled agents that interpret imagery, connect it with language, and execute structured analytical tasks. Extending these capabilities t…
GeoVLM-R1: Reinforcement Fine-Tuning for Improved Remote Sensing Reasoning
Mustansar Fiaz, Hiyam Debary, Paolo Fraccaro +4
Recent advances in reinforcement learning (RL) have delivered strong reasoning capabilities in natural image domains, yet their potential for Earth Observation (EO) remains largely…
PrediTree: A Multi-Temporal Sub-meter Dataset of Multi-Spectral Imagery Aligned With Canopy Height Maps
Hiyam Debary, Mustansar Fiaz, Levente Klein
We present PrediTree, the first comprehensive open-source dataset designed for training and evaluating tree height prediction models at sub-meter resolution. This dataset combines…
HyRet-Change: A hybrid retentive network for remote sensing change detection
Mustansar Fiaz, Mubashir Noman, Hiyam Debary +2
Recently convolution and transformer-based change detection (CD) methods provide promising performance. However, it remains unclear how the local and global dependencies interact t…
InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman +3
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolut…
Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets
Daniya Najiha A. Kareem, Jean Lahoud, Mustansar Fiaz +2
Many practical medical imaging scenarios include categories that are under-represented but still crucial. The relevance of image recognition models to real-world applications lies…
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues
Sagar Soni, Akshay Dudhane, Hiyam Debary +8
Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and {r…
CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image Collections
Mohamed Fazli Imam, Rufael Fedaku Marew, Jameel Hassan +3
In the era of foundation models, CLIP has emerged as a powerful tool for aligning text & visual modalities into a common embedding space. However, the alignment objective used to t…
COSNet: A Novel Semantic Segmentation Network using Enhanced Boundaries in Cluttered Scenes
Muhammad Ali, Mamoona Javaid, Mubashir Noman +2
Automated waste recycling aims to efficiently separate the recyclable objects from the waste by employing vision-based systems. However, the presence of varying shaped objects havi…
Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing
Daniya Najiha Abdul Kareem, Mustansar Fiaz, Noa Novershtern +2
Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxe…
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
Asifullah Khan, Anabia Sohail, Mustansar Fiaz +11
Advances in deep learning are re-defining how visual data is processed and understand by the machines. Vision Transformers (ViTs) have recently demonstrated prominent performance i…
FANet: Feature Amplification Network for Semantic Segmentation in Cluttered Background
Muhammad Ali, Mamoona Javaid, Mubashir Noman +2
Existing deep learning approaches leave out the semantic cues that are crucial in semantic segmentation present in complex scenarios including cluttered backgrounds and translucent…
Medical Image Segmentation Using Directional Window Attention
Daniya Najiha Abdul Kareem, Mustansar Fiaz, Noa Novershtern +1
Accurate segmentation of medical images is crucial for diagnostic purposes, including cell segmentation, tumor identification, and organ localization. Traditional convolutional neu…
ChangeBind: A Hybrid Change Encoder for Remote Sensing Change Detection
Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal
Change detection (CD) is a fundamental task in remote sensing (RS) which aims to detect the semantic changes between the same geographical regions at different time stamps. Existin…
ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection
Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal +2
Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired…
DDAM-PS: Diligent Domain Adaptive Mixer for Person Search
Mohammed Khaleed Almansoori, Mustansar Fiaz, Hisham Cholakkal
Person search (PS) is a challenging computer vision problem where the objective is to achieve joint optimization for pedestrian detection and re-identification (ReID). Although pre…
SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation
Mustansar Fiaz, Moein Heidari, Rao Muhammad Anwer +1
Microscopic image segmentation is a challenging task, wherein the objective is to assign semantic labels to each pixel in a given microscopic image. While convolutional neural netw…
Remote Sensing Change Detection With Transformers Trained from Scratch
Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal +4
Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-train…
PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search
Mustansar Fiaz, Hisham Cholakkal, Sanath Narayan +2
Person search is a challenging problem with various real-world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery imag…
Brain MRI Segmentation using Rule-Based Hybrid Approach
Mustansar Fiaz, Kamran Ali, Abdul Rehman +2
Medical image segmentation being a substantial component of image processing plays a significant role to analyze gross anatomy, to locate an infirmity and to plan the surgical proc…
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends
Mustansar Fiaz, Arif Mahmood, Sajid Javed +1
In recent years visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has…
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches
Mustansar Fiaz, Arif Mahmood, Soon Ki Jung
Visual object tracking is an important computer vision problem with numerous real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-bas…