Search Results for author:
Found 11 papers, 7 papers with code
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
Recent advancements in deep learning have greatly enhanced 3D object recognition, but most models are limited to closed-set scenarios, unable to handle unknown samples in real-world applications.
Natural Language Fine-Tuning
Compared to SFT, NLFT does not increase the algorithmic complexity, maintaining O(n).
Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation
The fidelity enhancement module deforms the 3D mesh to match the input image.
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training
Efficient communication can enhance the overall performance of collaborative multi-agent reinforcement learning.
More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding
To address this task, we introduce GreenPLM, which leverages more text data to compensate for the lack of 3D data.
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition
Furthermore, to enhance the learning of semantic representation associated with stimulation tasks, a semantic consistency contrast module is proposed, aiming to maximize the semantic similarity of fNIRS and EEG.
Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oracles
For solving zero-sum games involving non-transitivity, a useful approach is to maintain a policy population to approximate the Nash Equilibrium (NE).
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
Notably, MiniGPT-3D gains an 8. 12 increase on GPT-4 evaluation score for the challenging object captioning task compared to ShapeLLM-13B, while the latter costs 160 total GPU-hours on 8 A800.
Ranked #1 on
3D Object Captioning
on Objaverse
PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Segmentation
Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge, due to a closed-set and static perspective of the real world, which would induce the intelligent agent to make bad decisions.
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking
In our work, we present Point-LGMask, a novel method to embed both local and global contexts with multi-ratio masking, which is quite effective for self-supervised feature learning of point clouds but is unfortunately ignored by existing pre-training works.
Ranked #4 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 5-way (10-shot)
(using extra training data)
CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion
In our work, we present CasFusionNet, a novel cascaded network for point cloud semantic scene completion by dense feature fusion.
