most citedBLINK: Multimodal Large Language Models Can See but Not Perceive

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025

MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation

Jihan Yao, Yushi Hu, Yujie Yi +10

Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align with human evaluation, especially for complex tasks th…

cs.CV2025

PointArena: Probing Multimodal Grounding Through Language-Guided Pointing

Long Cheng, Jiafei Duan, Yi Ru Wang +12

Pointing serves as a fundamental and intuitive mechanism for grounding language within visual contexts, with applications spanning robotics, assistive technologies, and interactive…

cs.CV2025

Eval3D: Interpretable and Fine-grained Evaluation for 3D Generation

Shivam Duggal, Yushi Hu, Oscar Michel +7

Despite the unprecedented progress in the field of 3D generation, current systems still often fail to produce high-quality 3D assets that are visually appealing and geometrically a…

cs.CV2024

Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models

Yushi Hu, Weijia Shi, Xingyu Fu +5

Humans draw to facilitate reasoning: we draw auxiliary lines when solving geometry problems; we mark and circle when reasoning on maps; we use sketches to amplify our ideas and rel…

cs.CV2024★ 1 cited

BLINK: Multimodal Large Language Models Can See but Not Perceive

Xingyu Fu, Yushi Hu, Bangzheng Li +7

We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations. Most of the Blink tasks c…