Search Results for author:
Found 9 papers, 5 papers with code
Adaptive Hinge Balance Loss for Document-Level Relation Extraction
In this paper, we propose to downweight the easy negatives by utilizing a distance between the classification threshold and the predicted score of each relation.
Ranked #2 on
Relation Extraction
on ReDocRED
Document-level Relation Extraction
Multi-Label Classification
+1
ADTR: Anomaly Detection Transformer with Feature Reconstruction
Second, CNN tends to reconstruct both normal samples and anomalies well, making them still hard to distinguish.
A Unified Model for Multi-class Anomaly Detection
For example, when learning a unified model for 15 categories in MVTec-AD, we surpass the second competitor on the tasks of both anomaly detection (from 88. 1% to 96. 5%) and anomaly localization (from 89. 5% to 96. 8%).
The Z-axis, X-axis, Weight and Disambiguation Methods for Constructing Local Reference Frame in 3D Registration: An Evaluation
The performance of these methods are comprehensively evaluated on six standard datasets with different application scenarios and nuisances.
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability.
Few-shot Object Counting with Similarity-Aware Feature Enhancement
This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i. e., described by one or several support images) occurring in the query image.
Ranked #2 on
Object Counting
on CARPK
PF-Net: Point Fractal Network for 3D Point Cloud Completion
Unlike existing point cloud completion networks, which generate the overall shape of the point cloud from the incomplete point cloud and always change existing points and encounter noise and geometrical loss, PF-Net preserves the spatial arrangements of the incomplete point cloud and can figure out the detailed geometrical structure of the missing region(s) in the prediction.
A Comprehensive Performance Evaluation for 3D Transformation Estimation Techniques
This paper evaluates eleven state-of-the-art transformation estimation proposals on both descriptor based and synthetic correspondences.
A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local Surface
Besides, an improved LRA is proposed for increasing the robustness of our SDASS to noise and varying mesh resolutions.
