Search Results for author:
Found 11 papers, 4 papers with code
Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion
In this paper, we propose a global-local MAV detector that can fuse both motion and appearance features for MAV detection under challenging conditions.
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working.
Geo6D: Geometric Constraints Learning for 6D Pose Estimation
Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters.
Uni6Dv2: Noise Elimination for 6D Pose Estimation
Uni6D is the first 6D pose estimation approach to employ a unified backbone network to extract features from both RGB and depth images.
Benchmarking Unsupervised Anomaly Detection and Localization
From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation.
Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation
They use a 2D CNN for RGB images and a per-pixel point cloud network for depth data, as well as a fusion network for feature fusion.
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.
Ranked #20 on
Anomaly Detection
on MVTec AD
Unsupervised Anomaly Detection
Weakly Supervised Defect Detection
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization
To facilitate the learning with only normal images, we propose a new pretext task called non-contrastive learning for the fine alignment stage.
Ranked #49 on
Anomaly Detection
on MVTec AD
Zero-Shot Instance Segmentation
We follow this motivation and propose a new task set named zero-shot instance segmentation (ZSI).
Background Learnable Cascade for Zero-Shot Object Detection
The major contributions for BLC are as follows: (i) we propose a multi-stage cascade structure named Cascade Semantic R-CNN to progressively refine the alignment between visual and semantic of ZSD; (ii) we develop the semantic information flow structure and directly add it between each stage in Cascade Semantic RCNN to further improve the semantic feature learning; (iii) we propose the background learnable region proposal network (BLRPN) to learn an appropriate word vector for background class and use this learned vector in Cascade Semantic R CNN, this design makes \Background Learnable" and reduces the confusion between background and unseen classes.
Ranked #6 on
Zero-Shot Object Detection
on PASCAL VOC'07
Short-Term Temporal Convolutional Networks for Dynamic Hand Gesture Recognition
Subsequently, in temporal analysis, we use TCNs to extract temporal features and employ improved Squeeze-and-Excitation Networks (SENets) to strengthen the representational power of temporal features from each TCNs' layers.
