4 citations · 7 across the 4 of their papers we have counts for
3 papers · 1 filter
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos
Shehan Munasinghe, Hanan Gani, Wenqi Zhu +4
Fine-grained alignment between videos and text is challenging due to complex spatial and temporal dynamics in videos. Existing video-based Large Multimodal Models (LMMs) handle bas…
PG-Video-LLaVA: Pixel Grounding Large Video-Language Models
Shehan Munasinghe, Rusiru Thushara, Muhammad Maaz +4
Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to vide…
Class-Aware Attention for Multimodal Trajectory Prediction
Bimsara Pathiraja, Shehan Munasinghe, Malshan Ranawella +3
Predicting the possible future trajectories of the surrounding dynamic agents is an essential requirement in autonomous driving. These trajectories mainly depend on the surrounding…