Conference Overview

The 2026 International Winter Conference on Vision, Language & Learning (WVLL 2026) convenes researchers and practitioners working at the intersection of computer vision, natural language processing, and multimodal learning. Across two days we will explore how to build efficient, responsible, and high-performing vision-language systems that advance understanding, interaction, and accessibility.

Event Snapshot

Key Areas of Exploration:

  • AI For Low-Resource Languages
  • Video And Speech Analysis For Low-Resource Languages
  • LLM and VLM Architectures and Neural Design
  • Parameter-Efficient Adaptation of Large Vision-Language Models
  • Applications in Vision-Language Models
  • Tiny VLMs: Efficient Multimodal AI at the Edge
  • New Benchmark Dataset & Evaluation Metrics
  • AI for Sign Language Understanding
  • Document Image Processing
  • Medical Data Analysis
  • Scene Text Detection And Recognition

WVLL 2026 fosters a rich exchange of ideas that can crystallize common problems and illuminate promising scientific paradigms in vision-language research. We aim to explicitly contrast competing frameworks, clarify essential research questions, and cultivate a stronger community around these shared interests. WVLL distinguishes itself by its balanced emphasis on theoretical advancements in model design and the practical, societal implications of their deployment, particularly in resource-constrained and specialized domains. We encourage the presentation of work-in-progress and forward-looking position papers to spark vibrant discussion and future breakthroughs.

Invited Speakers

Confirmed Speakers

Tentative Speakers

Diversity, Equity & Inclusion Plan

WVLL 2026 embeds diversity and inclusion across organizers, speakers, and attendees through concrete, realistic actions. Our committee spans multiple continents and balances academia with industry and NGO perspectives, creating natural mentorship pathways and technical breadth from computer vision to clinical AI. We are recruiting invited speakers through affinity groups and regional mailing lists to secure meaningful representation of women, non-binary scholars, and researchers based in the Global South. The gender-neutral CFP explicitly welcomes work on sign-language AI, low-resource languages, and edge deployment in underserved regions, while an optional mentored-review track pairs junior authors with experienced PC members. External sponsorships will fund travel stipends prioritized for students from low- and middle-income countries and for caregivers. Live captioning, wheelchair-accessible poster spacing, and an anonymous code-of-conduct reporting channel coordinated by our DEI chair will ensure a safe, inclusive environment, making diversity and broad participation integral to WVLL 2026 rather than an afterthought.

Estimated Number of Attendees

Given the growing interest in multimodal AI - especially low-resource language processing, efficient model adaptation, and applied vision-language systems - we anticipate 120–150 participants from academia and industry. This includes researchers, practitioners, and students focused on vision-language learning, efficient model design, and AI applications for underrepresented and resource-constrained domains.

Special Requirements and Technical Needs

WVLL 2026 is a two-day, in-person conference hosted at the University of North Texas. We request a standard A/V setup (projector with HDMI input, screen, microphones for speakers and audience), reliable internet to support live demos, and poster space for approximately 20–25 physical posters. We will also need a table area for interactive demos related to vision-language systems. The venue should provide wheelchair accessibility throughout.

Previous Editions

WVLL previously ran as a workshop at WACV 2024, focusing on vision-language learning for low-resource languages, parameter-efficient model adaptation, and applied multimodal AI. That edition received 14 submissions (3 accepted) with authors spanning Bangladesh, the United States, and India, and a reviewer pool of 32 experts. Building on this momentum, WVLL 2026 expands into a full conference to broaden reach, deepen technical exchange, and grow the community.

URL of previous workshop: https://wvll.github.io/2024

Brief Bios of Organizers

Fuad Rahman: Fuad Rahman, Ph.D., is an academician and entrepreneur who founded Apurba Technologies, specializing in machine learning. He is also an Adjunct Professor at the University of Arizona's BME Department. His company actively works on computerizing Bangla, a low-resource language, developing the first commercial Bangla OCR and screen reader. He has over 100 peer-reviewed publications.
Email: fuad@apurbatech.com | Website: apurbatech.com

Syed Akhter Hossain: Dr. Syed Akhter Hossain is a Dean & Professor at UCSI University. He has significantly advanced NLP research and has over 250 publications. A recipient of the Best Professor of IT Award (2012) and National ICT Award (2016), he notably developed a machine translator for Bangla Braille.
Email: deanfsit@daffodilvarsity.edu.bd | Website: https://faculty.daffodilvarsity.edu.bd/profile/swe/akhter.html

Tozammel Hossain: Dr. Tozammel Hossain is an Assistant Professor at the University of North Texas, specializing in applied machine learning, causal inference, and biomedical informatics. With a Ph.D. from Virginia Tech and postdoctoral experience at USC, he has contributed to high-impact projects funded by IARPA, DARPA, DHS, and USDA. He has published in leading journals and presented at top conferences.
Email: tozammel.hossain@unt.edu | Website: https://facultyinfo.unt.edu/faculty-profile?profile=kh0718

Haihua Chen: Dr. Haihua Chen is an Assistant Professor in the Department of Data Science and Health Informatics at the University of North Texas (UNT). He directs the Intelligent Data Engineering and Analytics (IDEA) Lab at UNT. His research interests include applied data science, data quality, information retrieval, natural language processing, text mining, legal artificial intelligence, and health informatics. Dr. Chen has co-authored over 50 publications in both Information Science and Computer Science. He serves as the co-editor of The Electronic Library and as a guest editor for journals such as Frontiers in Big Data and Information Discovery & Delivery. He is an active member of academic associations including ACM, IEEE, SIGIR, ASIS&T, and AAAI. Dr. Chen received his Ph.D. from the University of North Texas, M.S. from Wuhan University, China, and B.S. from Central China Normal University, China.
Email: Haihua.Chen@unt.edu | Website: https://idealabunt.github.io/home/haihua-chen.html

Syed Ashiqur Rahman: As a Data Scientist with over 10 years of experience, Syed specializes in Machine Learning, Deep Learning, and Big Data. His expertise includes optimization, statistical pattern recognition, analysis of single-cell genomics and multi-omics data. With a strong background in mathematics, statistics, and data analytics, he has a proven track record of interpreting and analyzing data to find patterns and provide actionable insights. At GSK he is solving real-world problems and contributing to data science in therapeutic research and development. Before joining GSK, he was a post-doctoral researcher in University of Pittsburgh. Syed received his Ph.D. in Computer Science.
Email: syed.ashiqur.rahman@gsk.com | Website: https://www.linkedin.com/in/syedashiqur-rahman/

Sheikh Abujar: Sheikh Abujar is a Ph.D. candidate in Computer Science at UAB, researching deep learning, vision-language models (VLMs), and clinical natural language processing. He interned at Samsung Research America (2024) and co-led impactful projects, including creating low-resource datasets like Bayanno (Bangla Speech) and IsharaLipi (Bangla Sign Language).
Email: sabujar@uab.edu | Website: https://sites.google.com/site/iamabujarsheikh

AKM Shahariar Azad Rabby: AKM Shahariar Azad Rabby is a Ph.D. candidate in Computer Science at the University of Alabama at Birmingham (UAB) and a Technical Lead (ML & Systems) at Apurba Technologies. His work focuses on computer vision, deep learning, and production machine learning systems, with particular emphasis on OCR, vision-language models, and MLOps. He has contributed to the development of large-scale OCR systems and low-resource language technologies for Bangla. He has authored over 46 peer-reviewed publications and has received multiple academic and professional recognitions.
Email: shahariar@rabby.dev | Website: rabby.dev

Confirmed Program Committee Members

Reviewer Organization
Sadia Afroz Gen™
Sandeep Bodduluri University of Alabama at Birmingham, USA
Tazin Afrin ETS
Abdus Sattar Daffodil International University, Bangladesh
Abu Kaisar Mohammad Masum Florida Institute of Technology, USA
Jagdish Chand Bansal South Asian University, India
Stephen Olatunde Olabiyisi Ladoke Akintola University of Technology, Nigeria
Sunil Kumar Khatri Amity University Tashkent, Uzbekistan
Yagyanath Rimal Pokhara University, Nepal
Ghalib Hussaiyn PayPal
Hasmot Ali Apurba Technologies Ltd
Md. Fahad Hossain Daffodil International University, Bangladesh
Mahmudul Hasan Comilla University, Bangladesh
Mohammad Mamun Or Rashid Jahangirnagar University, Bangladesh
Md Majedul Islam Kennesaw State University, USA
Md. Sanzidul Islam King Abdulaziz University, Saudi Arabia
Mirza Sami Deka Research & Development
Mohammad Shorif Uddin Jahangirnagar University, Bangladesh
Mouhaydine Tlemcani Universidade de Évora, Portugal
Muntaser Syed NVIDIA
Nabeel Mohammed North South University, Bangladesh
Naveed Mahmud Florida Institute of Technology, USA
Nushrat Jahan Ria Daffodil International University, Bangladesh
Lingzi Hong University of North Texas, USA
Ting Xiao University of North Texas, USA
Pratim Saha University of Alabama at Birmingham, USA
S.R. Subramanya National University (San Diego, USA) / Exskillence
S.M. Saiful Islam Badhon University of North Texas, USA
Saif Islam Charles Schwab
Sharun Akter Khushbu Daffodil International University, Bangladesh
Tanvir Ahmed University of Central Florida, USA
S.M. Mazharul Hoque Chowdhury University of North Texas, USA
Monjurul Huda Amazon