1 citations · 1 across the 7 of their papers we have counts for
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Mitigating Positional Leakage in 3D Masked Autoencoders for Robust Representation Learning
Xu Yan, Huiqun Wang, Chen Wang +2
Masked autoencoding has emerged as a prominent paradigm for self-supervised learning on 3D point clouds, achieving competitive performance across downstream tasks. Unlike its 2D co…
EgoMind: Activating Spatial Cognition through Linguistic Reasoning in MLLMs
Zhenghao Chen, Huiqun Wang, Di Huang
Multimodal large language models (MLLMs) are increasingly being applied to spatial cognition tasks, where they are expected to understand and interact with complex environments. Mo…
CoVFT: Context-aware Visual Fine-tuning for Multimodal Large Language Models
Nan Zhou, Huiqun Wang, Yaoyan Zheng +1
Multimodal large language models (MLLMs) achieve remarkable progress in cross-modal perception and reasoning, yet a fundamental question remains unresolved: should the vision encod…
Implicit Modeling for Transferability Estimation of Vision Foundation Models
Yaoyan Zheng, Huiqun Wang, Nan Zhou +1
Transferability estimation identifies the best pre-trained models for downstream tasks without incurring the high computational cost of full fine-tuning. This capability facilitate…
Multi-modal Relation Distillation for Unified 3D Representation Learning
Huiqun Wang, Yiping Bao, Panwang Pan +4
Recent advancements in multi-modal pre-training for 3D point clouds have demonstrated promising results by aligning heterogeneous features across 3D shapes and their corresponding…
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization
Huiqun Wang, Ruijie Yang, Di Huang +1
Differentiable ARchiTecture Search (DARTS) uses a continuous relaxation of network representation and dramatically accelerates Neural Architecture Search (NAS) by almost thousands…
PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation
Guangyuan Zhou, Huiqun Wang, Jiaxin Chen +1
RGB-D based 6D pose estimation has recently achieved remarkable progress, but still suffers from two major limitations: (1) ineffective representation of depth data and (2) insuffi…