13 citations · 29 across the 13 of their papers we have counts for
11 papers · 1 filter
Chat-Scene++: Exploiting Context-Rich Object Identification for 3D LLM
Haifeng Huang, Yilun Chen, Zehan Wang +2
Recent advancements in multi-modal large language models (MLLMs) have shown strong potential for 3D scene understanding. However, existing methods struggle with fine-grained object…
KiToke: Distribution-Aware Token Compression for Video Large Language Models
Haifeng Huang, Yang Li, Biyin Xu
Video Large Language Models (Video LLMs) achieve strong performance on video understanding tasks but suffer from high inference costs due to the large number of visual tokens. We s…
MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations
Ruiyuan Lyu, Jingli Lin, Tai Wang +8
With the emergence of LLMs and their integration with other data modalities, multi-modal 3D perception attracts more attention due to its connectivity to the physical world and mak…
FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion
Zehan Wang, Ziang Zhang, Xize Cheng +8
Unified multi-model representation spaces are the foundation of multimodal understanding and generation. However, the billions of model parameters and catastrophic forgetting probl…
Grounded 3D-LLM with Referent Tokens
Yilun Chen, Shuai Yang, Haifeng Huang +5
Prior studies on 3D scene understanding have primarily developed specialized models for specific tasks or required task-specific fine-tuning. In this study, we propose Grounded 3D-…
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…