activity
20192026
most citedLRM: Large Reconstruction Model for Single Image to 3D

31 citations · 66 across the 31 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.CV2025

More Than the Final Answer: Improving Visual Extraction and Logical Consistency in Vision-Language Models

Hoang Anh Just, Yifei Fan, Handong Zhao +6

Reinforcement learning from verifiable rewards (RLVR) has recently been extended from text-only LLMs to vision-language models (VLMs) to elicit long-chain multimodal reasoning. How…

cs.HC2025★ 1 cited

MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces

Reuben A. Luera, Ryan Rossi, Franck Dernoncourt +12

In an ideal design pipeline, user interface (UI) design is intertwined with user research to validate decisions, yet studies are often resource-constrained during early exploration…

cs.CV2025

Multimodal LLMs as Customized Reward Models for Text-to-Image Generation

Shijie Zhou, Ruiyi Zhang, Huaisheng Zhu +5

We introduce LLaVA-Reward, an efficient reward model designed to automatically evaluate text-to-image (T2I) generations across multiple perspectives, leveraging pretrained multimod…

cs.CV2025

MS4UI: A Dataset for Multi-modal Summarization of User Interface Instructional Videos

Yuan Zang, Hao Tan, Seunghyun Yoon +5

We study multi-modal summarization for instructional videos, whose goal is to provide users an efficient way to learn skills in the form of text instructions and key video frames.…

cs.CV2025

Towards Visual Text Grounding of Multimodal Large Language Model

Ming Li, Ruiyi Zhang, Jian Chen +7

Despite the existing evolution of Multimodal Large Language Models (MLLMs), a non-neglectable limitation remains in their struggle with visual text grounding, especially in text-ri…

cs.LG2025

From Selection to Generation: A Survey of LLM-based Active Learning

Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31

Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…