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From the 1 of 10 linked papers with an AI index.

most citedEfficient Multimodal Large Language Models: A Survey

25 citations · 25 across the 6 of their papers we have counts for

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cs.CV2026

Towards Spatial Supersensing in the Wild

Tianjun Gu, Tianyu Xin, Kuan Zhang +12

The paper introduces VSI‑Super‑Wild, a large benchmark of real‑world long videos with human‑verified QA pairs to evaluate how well multimodal models can track and reason about agen…

cs.CV2026

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse

Kuan Zhang, Dongchen Liu, Qiyue Zhao +12

The real world unfolds along a single set of physics laws, yet human intelligence demonstrates a remarkable capacity to generalize experiences from this singular physical existence…

cs.CV2026

Vision-language models lag human performance on physical dynamics and intent reasoning

Tianjun Gu, Jingyu Gong, Zhizhong Zhang +4

Spatial intelligence is central to embodied cognition, yet contemporary AI systems still struggle to reason about physical interactions in open-world human environments. Despite st…

cs.CV202625 cited

Efficient Multimodal Large Language Models: A Survey

Yizhang Jin, Jian Li, Yexin Liu +10

In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.…

cs.CV2025

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning

Sen Wang, Shao Zeng, Tianjun Gu +8

Low-level enhancement and high-level visual understanding in low-light vision have traditionally been treated separately. Low-light enhancement improves image quality for downstrea…