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Found 10 papers, 7 papers with code
POS-BERT: Point Cloud One-Stage BERT Pre-Training
We propose to use the dynamically updated momentum encoder as the tokenizer, which is updated and outputs the dynamic supervision signal along with the training process.
MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection
In this paper, we introduce a simple framework for Monocular DEtection with depth-aware TRansformer, named MonoDETR.
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning
Especially, we develop a variant of ViT for 3D point cloud feature extraction, which also achieves comparable results with existing backbones when combined with our framework, and visualization of the attention maps show that our model does understand the point cloud by combining the global shape information and multiple local structural information, which is consistent with the inspiration of our representation learning method.
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts
Contrastive Vision-Language Pre-training (CLIP) has drown 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.
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 #12 on
3D Part Segmentation
on ShapeNet-Part
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
To further enhance CLIP's few-shot capability, CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and significantly improves the performance for few-shot classification.
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Large-scale contrastive vision-language pre-training has shown significant progress in visual representation learning.
Dual-stream Network for Visual Recognition
Transformers with remarkable global representation capacities achieve competitive results for visual tasks, but fail to consider high-level local pattern information in input images.
End-to-End Object Detection with Adaptive Clustering Transformer
In this paper, a novel variant of transformer named Adaptive Clustering Transformer(ACT) has been proposed to reduce the computation cost for high-resolution input.
