1 citations · 1 across the 8 of their papers we have counts for
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Automated Borehole Core Analysis with Report-Derived Weak Labels and Supervised Crack Segmentation
Usama Imdad, Ali Khan, Luke Lu +2
Borehole archives commonly contain core tray photographs and corresponding digital log reports, but no native pixel-level crack annotations. We investigate two complementary approa…
MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding
Basit Alawode, Arif Mahmood, Muaz Khalifa Al-Radi +6
Whole Slide Images (WSIs) exhibit hierarchical structure, where diagnostic information emerges from cellular morphology, regional tissue organization, and global context. Existing…
MUOT_3M: A 3 Million Frame Multimodal Underwater Benchmark and the MUTrack Tracking Method
Ahsan Baidar Bakht, Mohamad Alansari, Muhayy Ud Din +5
Underwater Object Tracking (UOT) is crucial for efficient marine robotics, large scale ecological monitoring, and ocean exploration; however, progress has been hindered by the scar…
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Shahad Albastaki, Anabia Sohail, Iyyakutti Iyappan Ganapathi +6
In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a sing…
AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis
Basit Alawode, Iyyakutti Iyappan Ganapathi, Sajid Javed +3
The preservation of aquatic biodiversity is critical in mitigating the effects of climate change. Aquatic scene understanding plays a pivotal role in aiding marine scientists in th…
Implicit to Explicit Entropy Regularization: Benchmarking ViT Fine-tuning under Noisy Labels
Maria Marrium, Arif Mahmood, Mohammed Bennamoun
Automatic annotation of large-scale datasets can introduce noisy training data labels, which adversely affect the learning process of deep neural networks (DNNs). Consequently, Noi…
Predicting the Best of N Visual Trackers
Basit Alawode, Sajid Javed, Arif Mahmood +1
We observe that the performance of SOTA visual trackers surprisingly strongly varies across different video attributes and datasets. No single tracker remains the best performer ac…
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment
Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi +3
This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology…