collaborators

13 papers

cs.CV2026

Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

Md. Safirur Rashid, Sabbir Ahmed, Muhammad Usama Islam +2

Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large annotated datasets, which is…

cs.CV2026

Virtual Try-On for Cultural Clothing: A Benchmarking Study

Muhammad Tausif Ul Islam, Shahir Awlad, Sameen Yeaser Adib +3

Although existing virtual try-on systems have made significant progress with the advent of diffusion models, the current benchmarks of these models are based on datasets that are d…

cs.SD2026

SpectroFusion-ViT: A Lightweight Transformer for Speech Emotion Recognition Using Harmonic Mel-Chroma Fusion

Faria Ahmed, Rafi Hassan Chowdhury, Fatema Tuz Zohora Moon +1

Speech is a natural means of conveying emotions, making it an effective method for understanding and representing human feelings. Reliable speech emotion recognition (SER) is centr…

cs.CV2026

From Lightweight CNNs to SpikeNets: Benchmarking Accuracy-Energy Tradeoffs with Pruned Spiking SqueezeNet

Radib Bin Kabir, Tawsif Tashwar Dipto, Mehedi Ahamed +2

Spiking Neural Networks (SNNs) are increasingly studied as energy-efficient alternatives to Convolutional Neural Networks (CNNs), particularly for edge intelligence. However, prior…

cs.CV2025

DExNet: Combining Observations of Domain Adapted Critics for Leaf Disease Classification with Limited Data

Sabbir Ahmed, Md. Bakhtiar Hasan, Tasnim Ahmed +1

While deep learning-based architectures have been widely used for correctly detecting and classifying plant diseases, they require large-scale datasets to learn generalized feature…

cs.CV2025

VisionTrap: Unanswerable Questions On Visual Data

Asir Saadat, Syem Aziz, Shahriar Mahmud +2

Visual Question Answering (VQA) has been a widely studied topic, with extensive research focusing on how VLMs respond to answerable questions based on real-world images. However, t…