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Found 7 papers, 4 papers with code
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
Recently, large-scale pre-trained models such as Segment-Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) have demonstrated remarkable success and revolutionized the field of computer vision.
Weakly Supervised 3D Instance Segmentation without Instance-level Annotations
3D semantic scene understanding tasks have achieved great success with the emergence of deep learning, but often require a huge amount of manually annotated training data.
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection.
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision
Instance segmentation on 3D point clouds has been attracting increasing attention due to its wide applications, especially in scene understanding areas.
Ranked #20 on
3D Instance Segmentation
on ScanNet(v2)
Explaining Deepfake Detection by Analysing Image Matching
Besides the supervision of binary labels, deepfake detection models implicitly learn artifact-relevant visual concepts through the FST-Matching (i. e. the matching fake, source, target images) in the training set.
Learning Regional Purity for Instance Segmentation on 3D Point Clouds
In this paper, we define a novel concept of “regional purity” as the percentage of neighboring points belonging to the same instance within a fixed-radius 3D space.
Ranked #13 on
3D Instance Segmentation
on ScanNet(v2)
ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language
We present a system called ACE for Automatic Colloquialism and Errors detection for written Chinese.
