77 citations · 125 across the 23 of their papers we have counts for
11 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…
Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner
Yuzhang Shang, Bingxin Xu, Weitai Kang +7
Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface l…
Removing Distributional Discrepancies in Captions Improves Image-Text Alignment
Yuheng Li, Haotian Liu, Mu Cai +5
In this paper, we introduce a model designed to improve the prediction of image-text alignment, targeting the challenge of compositional understanding in current visual-language mo…
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…
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…
Yo'LLaVA: Your Personalized Language and Vision Assistant
Thao Nguyen, Haotian Liu, Yuheng Li +3
Large Multimodal Models (LMMs) have shown remarkable capabilities across a variety of tasks (e.g., image captioning, visual question answering). While broad, their knowledge remain…
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…
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
Yuzhang Shang, Mu Cai, Bingxin Xu +2
Large Multimodal Models (LMMs) have shown significant visual reasoning capabilities by connecting a visual encoder and a large language model. LMMs typically take in a fixed and la…
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…