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20232026
most citedLMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models

32 citations · 40 across the 24 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CV2025

Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding

Yuanhan Zhang, Yunice Chew, Yuhao Dong +3

Human intelligence requires correctness and robustness, with the former being foundational for the latter. In video understanding, correctness ensures the accurate interpretation o…

cs.CV2025

High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning

Xinyu Huang, Yuhao Dong, Weiwei Tian +3

State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which ar…

cs.CV2025

ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models

Hongbo Liu, Jingwen He, Yi Jin +11

Cinematography, the fundamental visual language of film, is essential for conveying narrative, emotion, and aesthetic quality. While recent Vision-Language Models (VLMs) demonstrat…

cs.CV2025

Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning

Shulin Tian, Ruiqi Wang, Hongming Guo +7

We introduce Ego-R1, a novel framework for reasoning over ultra-long (i.e., in days and weeks) egocentric videos, which leverages a structured Chain-of-Tool-Thought (CoTT) process,…

cs.CV2025

EgoLife: Towards Egocentric Life Assistant

Jingkang Yang, Shuai Liu, Hongming Guo +19

We introduce EgoLife, a project to develop an egocentric life assistant that accompanies and enhances personal efficiency through AI-powered wearable glasses. To lay the foundation…

cs.CV2025

Ola: Pushing the Frontiers of Omni-Modal Language Model

Zuyan Liu, Yuhao Dong, Jiahui Wang +4

Recent advances in large language models, particularly following GPT-4o, have sparked increasing interest in developing omni-modal models capable of understanding more modalities.…

cs.CV2025

Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Shaoyuan Xie, Lingdong Kong, Yuhao Dong +5

Recent advancements in Vision-Language Models (VLMs) have sparked interest in their use for autonomous driving, particularly in generating interpretable driving decisions through n…