activity
20222025
most citedIntentGPT: Few-shot Intent Discovery with Large Language Models

5 citations · 11 across the 12 of their papers we have counts for

collaborators

13 papers

cs.LG2025★ 1 cited

Grounding Computer Use Agents on Human Demonstrations

Aarash Feizi, Shravan Nayak, Xiangru Jian +14

Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web…

cs.CV2025

Scope: Selective Cross-modal Orchestration of Visual Perception Experts

Tianyu Zhang, Suyuchen Wang, Chao Wang +5

Vision-language models (VLMs) benefit from multiple vision encoders, but naively stacking them yields diminishing returns while multiplying inference costs. We propose SCOPE, a Mix…

cs.CL2025

BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning

Ahmed Masry, Abhay Puri, Masoud Hashemi +13

Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although current vision-language models (VLMs) ha…

cs.CV2025

Rendering-Aware Reinforcement Learning for Vector Graphics Generation

Juan A. Rodriguez, Haotian Zhang, Abhay Puri +12

Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-language models (VLMs) have enabled high-qua…

cs.CV2025

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

Shravan Nayak, Xiangru Jian, Kevin Qinghong Lin +11

Autonomous agents that navigate Graphical User Interfaces (GUIs) to automate tasks like document editing and file management can greatly enhance computer workflows. While existing…

cs.CL2025★ 1 cited

AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding

Ahmed Masry, Juan A. Rodriguez, Tianyu Zhang +19

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges on having a good connector that maps vi…

cs.CL2024

Too Big to Fool: Resisting Deception in Language Models

Mohammad Reza Samsami, Mats Leon Richter, Juan Rodriguez +3

Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. This paper investigates this interplay by…

cs.LG2024★ 1 cited

BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks

Juan Rodriguez, Xiangru Jian, Siba Smarak Panigrahi +40

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and sum…

cs.CL2024★ 5 cited

IntentGPT: Few-shot Intent Discovery with Large Language Models

Juan A. Rodriguez, Nicholas Botzer, David Vazquez +3

In today's digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to virtual assistants. In these dialogues, it is impor…

cs.AI2024

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation

Gaurav Sahu, Abhay Puri, Juan Rodriguez +11

Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset w…

cs.CV2023★ 1 cited

StarVector: Generating Scalable Vector Graphics Code from Images and Text

Juan A. Rodriguez, Abhay Puri, Shubham Agarwal +6

Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation methods have focused on curve-based vectoriza…

cs.CV2022★ 1 cited

OCR-VQGAN: Taming Text-within-Image Generation

Juan A. Rodriguez, David Vazquez, Issam Laradji +2

Synthetic image generation has recently experienced significant improvements in domains such as natural image or art generation. However, the problem of figure and diagram generati…

cs.CV2022★ 1 cited

TrackNet: A Triplet metric-based method for Multi-Target Multi-Camera Vehicle Tracking

David Serrano, Francesc Net, Juan Antonio Rodríguez +1

We present TrackNet, a method for Multi-Target Multi-Camera (MTMC) vehicle tracking from traffic video sequences. Cross-camera vehicle tracking has proved to be a challenging task…