Search Results for author:
Found 25 papers, 16 papers with code
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning.
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
To this end, we introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs.
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery
We further design an adaptive weight assignment approach that balances the generalization ability of the LLM and the domain expertise of the old CL model.
SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning
Sign language recognition (SLR) plays a vital role in facilitating communication for the hearing-impaired community.
HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis
Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids.
ImageBind-LLM: Multi-modality Instruction Tuning
During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder.
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video.
Ranked #5 on
3D Question Answering (3D-QA)
on 3D MM-Vet
Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks
However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes.
3D Semantic Segmentation
Few-shot 3D semantic segmentation
+1
Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
In this paper, we propose MUTR, a Multi-modal Unified Temporal transformer for Referring video object segmentation.
Personalize Segment Anything Model with One Shot
Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models.
Ranked #1 on
Personalized Segmentation
on PerSeg
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions.
Ranked #1 on
Training-free 3D Part Segmentation
on ShapeNet-Part
3D Point Cloud Classification
Training-free 3D Part Segmentation
+1
Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis
Performances on standard 3D point cloud benchmarks have plateaued, resulting in oversized models and complex network design to make a fractional improvement.
Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training
In this paper, we explore how the 2D modality can benefit 3D masked autoencoding, and propose Joint-MAE, a 2D-3D joint MAE framework for self-supervised 3D point cloud pre-training.
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
Ranked #2 on
3D Open-Vocabulary Instance Segmentation
on STPLS3D
Low-Cost Beamforming and DOA Estimation Based on One-Bit Reconfigurable Intelligent Surface
In this work, we consider the Direction-of-Arrival (DOA) estimation problem in a low-cost architecture where only one antenna as the receiver is aided by a reconfigurable intelligent surface (RIS).
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classification.
Ranked #4 on
Training-free 3D Point Cloud Classification
on ScanObjectNN
(using extra training data)
Training-free 3D Point Cloud Classification
Transfer Learning
+1
Can Language Understand Depth?
To our best knowledge, we are the first to conduct zero-shot adaptation from the semantic language knowledge to quantified downstream tasks and perform zero-shot monocular depth estimation.
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
By fine-tuning on downstream tasks, Point-M2AE achieves 86. 43% accuracy on ScanObjectNN, +3. 36% to the second-best, and largely benefits the few-shot classification, part segmentation and 3D object detection with the hierarchical pre-training scheme.
Ranked #4 on
3D Point Cloud Linear Classification
on ModelNet40
(using extra training data)
A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System
A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS.
MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection
In this paper, we introduce the first DETR framework for Monocular DEtection with a depth-guided TRansformer, named MonoDETR.
3D Object Detection From Monocular Images
Autonomous Driving
+3
Reconfigurable Intelligent Surface Aided Sparse DOA Estimation Method With Non-ULA
The direction of arrival (DOA) estimation problem is addressed in this letter.
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts
Contrastive Language-Image Pre-training (CLIP) has drawn increasing attention recently for its transferable visual representation learning.
PointCLIP: Point Cloud Understanding by CLIP
On top of that, we design an inter-view adapter to better extract the global feature and adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in 2D.
Ranked #3 on
3D Open-Vocabulary Instance Segmentation
on STPLS3D
3D Open-Vocabulary Instance Segmentation
Few-Shot Learning
+6
DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion
We reverse the conventional design of applying convolution on voxels and attention to points.
Ranked #36 on
3D Part Segmentation
on ShapeNet-Part
Improved Heatmap-based Landmark Detection
This paper proposes a landmark detection network for detecting sutures in endoscopic pictures, which solves the problem of a variable number of suture points in the images.
