13 citations · 29 across the 13 of their papers we have counts for
7 papers · 1 filter
Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding
Haifeng Huang, Yang Zhao, Zehan Wang +2
Temporal Video Grounding (TVG) aims to localize the temporal boundary of a specific segment in an untrimmed video based on a given language query. Since datasets in this domain are…
Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers
Haifeng Huang, Yilun Chen, Zehan Wang +8
Recent advancements in 3D Large Language Models (LLMs) have demonstrated promising capabilities for 3D scene understanding. However, previous methods exhibit deficiencies in genera…
Extending Multi-modal Contrastive Representations
Zehan Wang, Ziang Zhang, Luping Liu +4
Multi-modal contrastive representation (MCR) of more than three modalities is critical in multi-modal learning. Although recent methods showcase impressive achievements, the high d…
Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes
Zehan Wang, Haifeng Huang, Yang Zhao +2
3D scene understanding has gained significant attention due to its wide range of applications. However, existing methods for 3D scene understanding are limited to specific downstre…
3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding
Zehan Wang, Haifeng Huang, Yang Zhao +5
3D visual grounding aims to localize the target object in a 3D point cloud by a free-form language description. Typically, the sentences describing the target object tend to provid…
Distilling Coarse-to-Fine Semantic Matching Knowledge for Weakly Supervised 3D Visual Grounding
Zehan Wang, Haifeng Huang, Yang Zhao +5
3D visual grounding involves finding a target object in a 3D scene that corresponds to a given sentence query. Although many approaches have been proposed and achieved impressive p…
Connecting Multi-modal Contrastive Representations
Zehan Wang, Yang Zhao, Xize Cheng +8
Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically aligned shared space. This paradigm shows remarkable generalization ability…