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…
OV-SCAN: Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection
Adrian Chow, Evelien Riddell, Yimu Wang +2
Open-vocabulary 3D object detection for autonomous driving aims to detect novel objects beyond the predefined training label sets in point cloud scenes. Existing approaches achieve…
Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimation
Yimu Wang, Evelien Riddell, Adrian Chow +2
Existing vision-language model (VLM)-based methods for out-of-distribution (OOD) detection typically rely on similarity scores between input images and in-distribution (ID) text pr…
Rethinking the Mixture of Vision Encoders Paradigm for Enhanced Visual Understanding in Multimodal LLMs
Mozhgan Nasr Azadani, James Riddell, Sean Sedwards +1
Mixture of Vision Encoders (MoVE) has emerged as a powerful approach to enhance the fine-grained visual understanding of multimodal large language models (MLLMs), improving their a…
VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation
Chengjie Huang, Vahdat Abdelzad, Sean Sedwards +1
Input aggregation is a simple technique used by state-of-the-art LiDAR 3D object detectors to improve detection. However, increasing aggregation is known to have diminishing return…
SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling
Chengjie Huang, Vahdat Abdelzad, Sean Sedwards +1
We consider the problem of cross-sensor domain adaptation in the context of LiDAR-based 3D object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to gen…
SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction
Prarthana Bhattacharyya, Chengjie Huang, Krzysztof Czarnecki
This paper addresses motion forecasting in multi-agent environments, pivotal for ensuring safety of autonomous vehicles. Traditional as well as recent data-driven marginal trajecto…