3 citations · 8 across the 14 of their papers we have counts for
16 papers
Beyond Pattern Matching in 3D MLLMs: Part-Level Functional Reasoning for Generalizable Recognition
Weihong Li, Hao Jiang, Yaohua Zha +2
3D MLLMs recognize objects via pattern matching, which fails when geometrically similar objects require functional reasoning to disambiguate (e.g., dresser vs. wardrobe). Our per-s…
DST-Net: Decomposed Spatio-Temporal Network for Time Series Forecasting
Yaohua Zha, Hai Wu, Shu-Tao Xia
Series decomposition has emerged as a widely acknowledged paradigm in deep time series forecasting models. However, existing approaches predominantly employ a homogeneous modeling…
CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting
Yaohua Zha, Chunlin Fan, Peiyuan Liu +4
Multi-channel time-series data, prevalent across diverse applications, is characterized by significant heterogeneity in its different channels. However, existing forecasting models…
CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection
Yaohua Zha, Xue Yuerong, Chunlin Fan +4
Deep learning-based 3D anomaly detection methods have demonstrated significant potential in industrial manufacturing. However, many approaches are specifically designed for anomaly…
PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter
Yaohua Zha, Yanzi Wang, Hang Guo +7
Applying pre-trained models to assist point cloud understanding has recently become a mainstream paradigm in 3D perception. However, existing application strategies are straightfor…
Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning
Jinpeng Wang, Tianci Luo, Yaohua Zha +7
Visual In-Context Learning (VICL) enables adaptively solving vision tasks by leveraging pixel demonstrations, mimicking human-like task completion through analogy. Prompt selection…
MambaIRv2: Attentive State Space Restoration
Hang Guo, Yong Guo, Yaohua Zha +5
The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency. However, the inherent causa…
Towards Scalable Semantic Representation for Recommendation
Taolin Zhang, Junwei Pan, Jinpeng Wang +9
With recent advances in large language models (LLMs), there has been emerging numbers of research in developing Semantic IDs based on LLMs to enhance the performance of recommendat…
ReFIR: Grounding Large Restoration Models with Retrieval Augmentation
Hang Guo, Tao Dai, Zhihao Ouyang +4
Recent advances in diffusion-based Large Restoration Models (LRMs) have significantly improved photo-realistic image restoration by leveraging the internal knowledge embedded withi…
Point Cloud Mixture-of-Domain-Experts Model for 3D Self-supervised Learning
Yaohua Zha, Tao Dai, Hang Guo +4
Point clouds, as a primary representation of 3D data, can be categorized into scene domain point clouds and object domain point clouds. Point cloud self-supervised learning (SSL) h…
Large Point-to-Gaussian Model for Image-to-3D Generation
Longfei Lu, Huachen Gao, Tao Dai +4
Recently, image-to-3D approaches have significantly advanced the generation quality and speed of 3D assets based on large reconstruction models, particularly 3D Gaussian reconstruc…
Pre-training Point Cloud Compact Model with Partial-aware Reconstruction
Yaohua Zha, Yanzi Wang, Tao Dai +1
The pre-trained point cloud model based on Masked Point Modeling (MPM) has exhibited substantial improvements across various tasks. However, two drawbacks hinder their practical ap…
Invertible Residual Rescaling Models
Jinmin Li, Tao Dai, Yaohua Zha +6
Invertible Rescaling Networks (IRNs) and their variants have witnessed remarkable achievements in various image processing tasks like image rescaling. However, we observe that IRNs…
LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling
Yaohua Zha, Naiqi Li, Yanzi Wang +6
The pre-trained point cloud model based on Masked Point Modeling (MPM) has exhibited substantial improvements across various tasks. However, these models heavily rely on the Transf…
Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
Yaohua Zha, Huizhen Ji, Jinmin Li +5
Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifica…
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
Yaohua Zha, Jinpeng Wang, Tao Dai +3
Pre-trained point cloud models have found extensive applications in 3D understanding tasks like object classification and part segmentation. However, the prevailing strategy of ful…