1 citations · 1 across the 10 of their papers we have counts for
12 papers
Where Does the Answer Come From? Benchmarking View-Level Visual Evidence Identification in Multi-View MLLMs for Autonomous Driving
Yimu Wang, Yee Man Choi, Barry Zhang +3
Multimodal large language models (MLLMs) achieve strong results on visual reasoning benchmarks, but answer accuracy alone does not indicate whether a model relied on the correct vi…
VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence
Mozhgan Nasr Azadani, Yimu Wang, Yongpeng Zhu +5
Establishing a clear link between model predictions and the visual evidence that supports them is critical for transparency and reliability in multimodal reasoning, yet current mul…
How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+
Mei Qi Tang, Sean Sedwards, Chengjie Huang +1
The impact of snowfall on 3D object detection performance remains underexplored. Conducting such an evaluation requires a dataset with sufficient labelled data from both weather co…
HAWAII: Hierarchical Visual Knowledge Transfer for Efficient Vision-Language Models
Yimu Wang, Mozhgan Nasr Azadani, Sean Sedwards +1
Improving the visual understanding ability of vision-language models (VLMs) is crucial for enhancing their performance across various tasks. While using multiple pretrained visual…
MFSeg: Efficient Multi-frame 3D Semantic Segmentation
Chengjie Huang, Krzysztof Czarnecki
We propose MFSeg, an efficient multi-frame 3D semantic segmentation framework. By aggregating point cloud sequences at the feature level and regularizing the feature extraction and…
LEO-MINI: An Efficient Multimodal Large Language Model using Conditional Token Reduction and Mixture of Multi-Modal Experts
Yimu Wang, Mozhgan Nasr Azadani, Sean Sedwards +1
Redundancy of visual tokens in multi-modal large language models (MLLMs) significantly reduces their computational efficiency. Recent approaches, such as resamplers and summarizers…