16 citations · 23 across the 18 of their papers we have counts for
8 papers · 1 filter
TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Mu Cai, Reuben Tan, Jianrui Zhang +12
Understanding fine-grained temporal dynamics is crucial for multimodal video comprehension and generation. Due to the lack of fine-grained temporal annotations, existing video benc…
Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos
Jianrui Zhang, Mu Cai, Yong Jae Lee
There has been growing sentiment recently that modern large multimodal models (LMMs) have addressed most of the key challenges related to short video comprehension. As a result, bo…
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
Mu Cai, Chenxu Luo, Yong Jae Lee +1
3D perception in LiDAR point clouds is crucial for a self-driving vehicle to properly act in 3D environment. However, manually labeling point clouds is hard and costly. There has b…
VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation
Bocheng Zou, Mu Cai, Jianrui Zhang +1
In the realm of vision models, the primary mode of representation is using pixels to rasterize the visual world. Yet this is not always the best or unique way to represent visual c…
CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts
Shubham Bharti, Shiyun Cheng, Jihyun Rho +5
We introduce CHARTOM, a visual theory-of-mind benchmark designed to evaluate multimodal large language models' capability to understand and reason about misleading data visualizati…
LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
Xiang Li, Cristina Mata, Jongwoo Park +8
Vision Language Models (VLMs) have recently been leveraged to generate robotic actions, forming Vision-Language-Action (VLA) models. However, directly adapting a pretrained VLM for…
Matryoshka Multimodal Models
Mu Cai, Jianwei Yang, Jianfeng Gao +1
Large Multimodal Models (LMMs) such as LLaVA have shown strong performance in visual-linguistic reasoning. These models first embed images into a fixed large number of visual token…
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples
Jianrui Zhang, Mu Cai, Tengyang Xie +1
We propose CounterCurate, a framework to comprehensively improve the visio-linguistic compositional reasoning capability for both contrastive and generative multimodal models. In p…