Portrait de Rabiul Awal

Rabiul Awal

Doctorat - UdeM
Superviseur⋅e principal⋅e
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Vision par ordinateur

Publications

The Promise of RL for Autoregressive Image Editing
Amirhossein Kazemnejad
Ge Ya Luo
Juan A. Rodriguez
Sai Rajeswar
We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learn… (voir plus)ing (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.
CTRL-O: Language-Controllable Object-Centric Visual Representation Learning
Aniket Rajiv Didolkar
Andrii Zadaianchuk
Maximilian Seitzer
Efstratios Gavves
Object-centric representation learning aims to decompose visual scenes into fixed-size vectors called "slots" or "object files", where each … (voir plus)slot captures a distinct object. Current state-of-the-art models have shown remarkable success in object discovery, particularly in complex real-world scenes, while also generalizing well to unseen domains. However, these models suffer from a key limitation: they lack controllability. Specifically, current object-centric models learn representations based on their preconceived understanding of objects and parts, without allowing user input to guide or modify which objects are represented. Introducing controllability into object-centric models could unlock a range of useful capabilities, such as enabling models to represent scenes at variable levels of granularity based on user specification. In this work, we propose a novel approach that conditions slot representations through guided decomposition, paired with a novel contrastive learning objective, to enable user-directed control over which objects are represented. Our method achieves such controllability without any mask supervision and successfully binds to user-specified objects in complex real-world scenes.
Rendering-Aware Reinforcement Learning for Vector Graphics Generation
Juan A. Rodriguez
Haotian Zhang
Abhay Puri
Rishav Pramanik
Pascal Wichmann
Arnab Mondal
Mohammad Reza Samsami
Perouz Taslakian
Sai Rajeswar
David Vazquez
Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-lang… (voir plus)uage models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
Xiangru Jian
Kevin Qinghong Lin
Juan A. Rodriguez
Montek Kalsi
M. Tamer Özsu
David Vazquez
Perouz Taslakian
Sai Rajeswar
Human Annotator
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
Xiangru Jian
Kevin Qinghong Lin
Juan A. Rodriguez
Montek Kalsi
M. T. ¨Ozsu
David Vazquez
Perouz Taslakian
Sai Rajeswar
Human Annotator
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation
David Vazquez
Juan A. Rodriguez
Perouz Taslakian
Sai Rajeswar
Understanding diverse web data and automating web development presents an exciting challenge for agentic AI. While existing benchmarks addre… (voir plus)ss isolated web-based tasks—such as website-based Visual Question Answering (VQA) and UI-to-code generation—they lack a unified evaluation suite for assessing web agents that interact with and reason about web environments. We introduce WebMMU, a large-scale benchmark for evaluating AI-driven web agents across multilingual website VQA, HTML/CSS/JavaScript code editing, and sketch-to-code generation. WebMMU provides a comprehensive evaluation suite with real-world website data, multi-step reasoning tasks, and functional UI understanding. Benchmarking state-of-the-art multimodal models on WebMMU reveals significant limitations in web-based reasoning, layout understanding, and structured code generation, particularly in preserving UI hierarchy, handling multilingual content, and producing robust, functional code. While most existing models are optimized for English-only settings, WebMMU highlights the challenges of cross-lingual adaptation in real-world web development. These findings expose critical gaps in current models’ ability to understand website structures, execute user instructions, and generate high-quality web code, underscoring the need for more advanced multimodal reasoning in AI-driven web understanding and development.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation
David Vazquez
Juan A. Rodriguez
Perouz Taslakian
Sai Rajeswar
ServiceNow
WebMMU Benchmark
Understanding diverse web data and automating web development presents an exciting challenge for agentic AI. While existing benchmarks addre… (voir plus)ss isolated web-based tasks—such as website-based Visual Question Answering (VQA) and UI-to-code generation—they lack a unified evaluation suite for assessing web agents that interact with and reason about web environments. We introduce WebMMU, a large-scale benchmark for evaluating AI-driven web agents across multilingual website VQA, HTML/CSS/JavaScript code editing, and sketch-to-code generation. WebMMU provides a comprehensive evaluation suite with real-world website data, multi-step reasoning tasks, and functional UI understanding. Benchmarking state-of-the-art multimodal models on WebMMU reveals significant limitations in web-based reasoning, layout understanding, and structured code generation, particularly in preserving UI hierarchy, handling multilingual content, and producing robust, functional code. While most existing models are optimized for English-only settings, WebMMU highlights the challenges of cross-lingual adaptation in real-world web development. These findings expose critical gaps in current models’ ability to understand website structures, execute user instructions, and generate high-quality web code, underscoring the need for more advanced multimodal reasoning in AI-driven web understanding and development.
BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks
Juan A. Rodriguez
Xiangru Jian
Siba Smarak Panigrahi
Abhay Puri
Akshay Kalkunte Suresh
François Savard
Ahmed Masry
Amirhossein Abaskohi
Pierre-Andre Noel
Mats Leon Richter
Saverio Vadacchino
Sanket Biswas … (voir 19 de plus)
Sara Shanian
Ying Zhang
Sathwik Tejaswi Madhusudhan
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Sepideh Kharaghani
Sean Hughes
M. Özsu
Issam Hadj Laradji
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (voir plus) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to relevant training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure that our data is high quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench,, a benchmark suite with 10 novel tasks where we carefully create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench, improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations revealed that participants preferred the outputs from models trained with BigDocs over those from GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning.
BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks
Juan A. Rodriguez
Xiangru Jian
Siba Smarak Panigrahi
Abhay Puri
Akshay Kalkunte Suresh
François Savard
Ahmed Masry
Amirhossein Abaskohi
Pierre-Andre Noel
Mats Leon Richter
Saverio Vadacchino
Sanket Biswas … (voir 23 de plus)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi Madhusudhan
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Sepideh Kharaghani
Sean Hughes
M. Özsu
Issam Hadj Laradji
Perouz Taslakian
David Vazquez
Sai Rajeswar
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
Xiangru Jian
Siba Smarak Panigrahi
Abhay Puri
Akshay Kalkunte
Franccois Savard
Ahmed Masry
Amirhossein Abaskohi
Pierre-Andre Noel
M. L. Richter
Saverio Vadacchino
Shubbam Agarwal
Sanket Biswas … (voir 23 de plus)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Sepideh Kharagani
Sean Hughes
M. Özsu
Issam Hadj Laradji
Spandanna Gella
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (voir plus) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
Xiangru Jian
Siba Smarak Panigrahi
Abhay Puri
Akshay Kalkunte
Franccois Savard
Ahmed Masry
Amirhossein Abaskohi
Pierre-Andre Noel
M. L. Richter
Saverio Vadacchino
Shubbam Agarwal
Sanket Biswas … (voir 23 de plus)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Sepideh Kharagani
Sean Hughes
M. Özsu
Issam Hadj Laradji
Spandanna Gella
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (voir plus) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
Xiangru Jian
Siba Smarak Panigrahi
Abhay Puri
Akshay Kalkunte
Franccois Savard
Ahmed Masry
Amirhossein Abaskohi
Pierre-Andre Noel
Mats Leon Richter
Saverio Vadacchino
Shubbam Agarwal
Sanket Biswas … (voir 23 de plus)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Sepideh Kharaghani
Sean Hughes
M. Özsu
Issam Hadj Laradji
Spandanna Gella
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (voir plus) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .