Aishwarya Agrawal

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

Rabiul Awal

Nicolas Chapados

M. T. ¨Ozsu

David Vazquez

Chris Pal

Perouz Taslakian

Spandana Gella

Sai Rajeswar

Human Annotator

2025-03-19

ArXiv (preprint)

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation

Rabiul Awal

Mahsa Massoud

Zichao Li

Aarash Feizi

Suyuchen Wang

Chris Pal

David Vazquez

Siva Reddy

Juan A. Rodriguez

Perouz Taslakian

Spandana Gella

Sai Rajeswar

Understanding diverse web data and automating web development presents an exciting challenge for agentic AI. While existing benchmarks addre… (see more)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.

2025-03-05

ICLR.cc/2025/Workshop/DL4C (published)

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

Rabiul Awal

Nicolas Chapados

M. T. ¨Ozsu

David Vazquez

Chris Pal

Perouz Taslakian

Spandana Gella

Sai Rajeswar

Human Annotator

2025-03-01

arXiv (published)

Assessing and Learning Alignment of Unimodal Vision and Language Models

Le Zhang

Qian Yang

How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment meth… (see more)ods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/

2024-12-05

ArXiv (preprint)

Assessing and Learning Alignment of Unimodal Vision and Language Models

Le Zhang

Qian Yang

How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment meth… (see more)ods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/

2024-12-05

ArXiv (preprint)

Assessing and Learning Alignment of Unimodal Vision and Language Models

Le Zhang

Qian Yang

How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment meth… (see more)ods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/

2024-12-05

ArXiv (preprint)

Improving Text-to-Image Consistency via Automatic Prompt Optimization

Oscar Mañas

Pietro Astolfi

Melissa Hall

Candace Ross

Jack Urbanek

Adina Williams

Adriana Romero Soriano

Michal Drozdzal

2024-12-03

TMLR (accepted)

Controlling Multimodal LLMs via Reward-guided Decoding

Oscar Mañas

Pierluca D'Oro

Koustuv Sinha

Adriana Romero Soriano

Michal Drozdzal

2024-10-10

NeurIPS.cc/2024/Workshop/AFM (poster)

CTRL-O: Language-Controllable Object-Centric Visual Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Rabiul Awal

Maximilian Seitzer

Efstratios Gavves

Object-centric representation learning aims to decompose visual scenes into fixed-size vectors called "slots" or "object files", where each … (see more)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.

2024-10-10

NeurIPS.cc/2024/Workshop/AFM (poster)

Enhancing Multi-Agent Multi-Modal Collaboration with Fine-Grained Reward Modeling

Qian Yang

Weixiang Yan

Multi-Modal Large Language Models (MLLMs) have significantly advanced multi-modal reasoning but still struggle with compositional reasoning … (see more)tasks. Multi-agent collaboration provides a promising solution by leveraging the distinct capabilities of different agents. Specifically, a decomposer agent to handle task breakdown and an answerer agent to generate responses. While there have been efforts to adaptively decompose tasks based on the answerer agent's capabilities, such as using in-context learning, these methods often prove insufficient for fully effective decomposition. We address this issue by enhancing collaboration through fine-grained reward modeling, where each generated sub-question is assigned a specialized reward without requiring extra annotation or tuning of a reward model. Our proposed method dynamically optimizes the decomposition process, enabling better alignment between agents. Experimental results on four vision-language tasks demonstrate consistent improvements, with a 5.5\% absolute increase in mean performance over traditional approaches. These findings highlight the efficacy of fine-grained reward modeling for enhancing multi-agent, multi-modal collaboration.

2024-10-10

NeurIPS.cc/2024/Workshop/AFM (poster)

Visual Language Alignment Tuning

Le Zhang

Qian Yang

2024-10-10

NeurIPS.cc/2024/Workshop/AFM (poster)