9 citations · 9 across the 6 of their papers we have counts for
7 papers
MemeCULT-1K: Benchmarking South Asian Cultural Context and Humor Understanding of Multimodal Models
Tawsif Tashwar Dipto, Mehedi Ahamed, Radib Bin Kabir +5
Meme understanding goes beyond recognizing visual content or literal text; it requires implicit cultural knowledge and pragmatic inference that most vision-language models still la…
BanglaWild: An In-the-Wild Bengali Scene Text Recognition Benchmark for OCR and Vision-Language Models
Sadab Shiper, Tawsif Tashwar Dipto, Mir Md Inzamam +1
In-the-wild Bengali scene text recognition is largely unmeasured: existing resources target handwritten documents or constrained sign-board parsing, report only aggregate edit-dist…
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…
Are ASR foundation models generalized enough to capture features of regional dialects for low-resource languages?
Tawsif Tashwar Dipto, Azmol Hossain, Rubayet Sabbir Faruque +12
Conventional research on speech recognition modeling relies on the canonical form for most low-resource languages while automatic speech recognition (ASR) for regional dialects is…
RegSpeech12: A Regional Corpus of Bengali Spontaneous Speech Across Dialects
Md. Rezuwan Hassan, Azmol Hossain, Kanij Fatema +13
The Bengali language, spoken extensively across South Asia and among diasporic communities, exhibits considerable dialectal diversity shaped by geography, culture, and history. Pho…
Script Fragmentation and Format: What Drives the English-Bengali Performance Gap in Open LLMs?
Shimanto Bhowmik, Tawsif Tashwar Dipto, Md Sazzad Islam +2
Bengali is spoken by more than 230 million people, yet no standardized instrument evaluates large language models (LLMs) on Bengali across the task categories used to benchmark fro…
Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition
Mehedi Ahamed, Radib Bin Kabir, Tawsif Tashwar Dipto +3
This study investigates the performance of few-shot learning (FSL) approaches in recognizing Bangla handwritten characters and numerals using limited labeled data. It demonstrates…